{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import joblib\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import model_selection\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.model_selection import cross_val_score\n",
    "from joblib import load\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>A1</th>\n",
       "      <th>Ea1</th>\n",
       "      <th>A2</th>\n",
       "      <th>Ea2</th>\n",
       "      <th>A3</th>\n",
       "      <th>Ea3</th>\n",
       "      <th>A4</th>\n",
       "      <th>Ea4</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "      <th>Fast_rxn1</th>\n",
       "      <th>Medium_rxn1</th>\n",
       "      <th>Slow_rxn1</th>\n",
       "      <th>Fast_rxn2</th>\n",
       "      <th>Medium_rxn2</th>\n",
       "      <th>Slow_rxn2</th>\n",
       "      <th>Reaction_order</th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>1933581</td>\n",
       "      <td>273</td>\n",
       "      <td>261.472219</td>\n",
       "      <td>194</td>\n",
       "      <td>0.044413</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.236088e-02</td>\n",
       "      <td>3.977472e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>1933582</td>\n",
       "      <td>273</td>\n",
       "      <td>596.074861</td>\n",
       "      <td>62</td>\n",
       "      <td>8.957436</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.985276e-06</td>\n",
       "      <td>7.871450e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>1933583</td>\n",
       "      <td>273</td>\n",
       "      <td>423.789150</td>\n",
       "      <td>55</td>\n",
       "      <td>904.812139</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.042629e-07</td>\n",
       "      <td>7.955867e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>1933584</td>\n",
       "      <td>273</td>\n",
       "      <td>0.014179</td>\n",
       "      <td>123</td>\n",
       "      <td>0.061371</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.618686e-03</td>\n",
       "      <td>8.963359e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>1933585</td>\n",
       "      <td>273</td>\n",
       "      <td>0.045442</td>\n",
       "      <td>179</td>\n",
       "      <td>1.544173</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.689157e-03</td>\n",
       "      <td>2.451002e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 258 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Temperature          A1  Ea1          A2  Ea2  A3  Ea3  \\\n",
       "1933581     1933581          273  261.472219  194    0.044413  141   0    0   \n",
       "1933582     1933582          273  596.074861   62    8.957436    9   0    0   \n",
       "1933583     1933583          273  423.789150   55  904.812139  184   0    0   \n",
       "1933584     1933584          273    0.014179  123    0.061371   74   0    0   \n",
       "1933585     1933585          273    0.045442  179    1.544173  121   0    0   \n",
       "\n",
       "         A4  Ea4  ...  cINT1_10800s  cINT1_14400s  Fast_rxn1  Medium_rxn1  \\\n",
       "1933581   0    0  ...  4.236088e-02  3.977472e-02          1            0   \n",
       "1933582   0    0  ...  9.985276e-06  7.871450e-06          1            0   \n",
       "1933583   0    0  ...  1.042629e-07  7.955867e-08          1            0   \n",
       "1933584   0    0  ...  8.618686e-03  8.963359e-03          0            0   \n",
       "1933585   0    0  ...  2.689157e-03  2.451002e-03          0            0   \n",
       "\n",
       "         Slow_rxn1  Fast_rxn2  Medium_rxn2  Slow_rxn2  \\\n",
       "1933581          0          0            0          1   \n",
       "1933582          0          0            1          0   \n",
       "1933583          0          1            0          0   \n",
       "1933584          1          0            0          1   \n",
       "1933585          1          0            1          0   \n",
       "\n",
       "                                            Reaction_order  Mechanism  \n",
       "1933581  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933582  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933583  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933584  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933585  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "\n",
       "[5 rows x 258 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv('../two_reactions_022624.csv')\n",
    "df_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 258)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mechanism\n",
       "1933581  (93, 254)\n",
       "1933582  (93, 254)\n",
       "1933583  (93, 254)\n",
       "1933584  (93, 254)\n",
       "1933585  (93, 254)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_raw_df = df_raw.iloc[:, -1:]\n",
    "y_raw_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "unique_values = y_raw_df['Mechanism'].unique()\n",
    "print(len(unique_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 258)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_raw.copy()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "conc_list = [col for col in df.columns if col.endswith('s')]\n",
    "# conc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299992</td>\n",
       "      <td>0.299837</td>\n",
       "      <td>0.299675</td>\n",
       "      <td>0.299514</td>\n",
       "      <td>0.299035</td>\n",
       "      <td>0.298564</td>\n",
       "      <td>0.298101</td>\n",
       "      <td>0.297645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006314</td>\n",
       "      <td>0.006981</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>0.007860</td>\n",
       "      <td>0.007791</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.006560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299978</td>\n",
       "      <td>0.299957</td>\n",
       "      <td>0.299935</td>\n",
       "      <td>0.299870</td>\n",
       "      <td>0.299805</td>\n",
       "      <td>0.299740</td>\n",
       "      <td>0.299675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299996</td>\n",
       "      <td>0.299926</td>\n",
       "      <td>0.299853</td>\n",
       "      <td>0.299780</td>\n",
       "      <td>0.299561</td>\n",
       "      <td>0.299344</td>\n",
       "      <td>0.299129</td>\n",
       "      <td>0.298914</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299989</td>\n",
       "      <td>0.299887</td>\n",
       "      <td>0.297811</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0.293877</td>\n",
       "      <td>0.288868</td>\n",
       "      <td>0.284657</td>\n",
       "      <td>0.281052</td>\n",
       "      <td>0.277897</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017600</td>\n",
       "      <td>0.016738</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>0.014708</td>\n",
       "      <td>0.013685</td>\n",
       "      <td>0.012807</td>\n",
       "      <td>0.012051</td>\n",
       "      <td>0.010814</td>\n",
       "      <td>0.009851</td>\n",
       "      <td>0.008435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299941</td>\n",
       "      <td>0.299417</td>\n",
       "      <td>0.290150</td>\n",
       "      <td>0.282960</td>\n",
       "      <td>0.277360</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>0.257948</td>\n",
       "      <td>0.252221</td>\n",
       "      <td>0.247715</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.003784</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>0.002632</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.001348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0     0.3  0.299999  0.299992  0.299837  0.299675  0.299514  0.299035   \n",
       "1     0.3  0.300000  0.299999  0.299978  0.299957  0.299935  0.299870   \n",
       "2     0.3  0.300000  0.299996  0.299926  0.299853  0.299780  0.299561   \n",
       "3     0.3  0.299989  0.299887  0.297811  0.295775  0.293877  0.288868   \n",
       "4     0.3  0.299941  0.299417  0.290150  0.282960  0.277360  0.265672   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  cINT1_3600s  \\\n",
       "0  0.298564  0.298101  0.297645  ...     0.006314     0.006981     0.007418   \n",
       "1  0.299805  0.299740  0.299675  ...     0.000503     0.000512     0.000509   \n",
       "2  0.299344  0.299129  0.298914  ...     0.000119     0.000117     0.000115   \n",
       "3  0.284657  0.281052  0.277897  ...     0.017600     0.016738     0.015892   \n",
       "4  0.257948  0.252221  0.247715  ...     0.004310     0.003784     0.003389   \n",
       "\n",
       "   cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  \\\n",
       "0     0.007767     0.007898     0.007860     0.007791     0.007491   \n",
       "1     0.000498     0.000495     0.000490     0.000485     0.000474   \n",
       "2     0.000111     0.000108     0.000106     0.000103     0.000099   \n",
       "3     0.014708     0.013685     0.012807     0.012051     0.010814   \n",
       "4     0.002952     0.002632     0.002379     0.002182     0.001874   \n",
       "\n",
       "   cINT1_10800s  cINT1_14400s  \n",
       "0      0.007172      0.006560  \n",
       "1      0.000466      0.000450  \n",
       "2      0.000095      0.000088  \n",
       "3      0.009851      0.008435  \n",
       "4      0.001653      0.001348  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_raw = df[conc_list]\n",
    "x_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a MinMaxScaler instance\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Transpose the DataFrame so that rows become columns for scaling\n",
    "x_df_transposed = x_df_raw.T\n",
    "\n",
    "# Scale the entire transposed DataFrame\n",
    "scaled_data = scaler.fit_transform(x_df_transposed)\n",
    "\n",
    "# Transpose the scaled data back to the original orientation\n",
    "x = pd.DataFrame(scaled_data.T, columns=x_df_raw.columns, index=x_df_raw.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333324</td>\n",
       "      <td>0.333152</td>\n",
       "      <td>0.332972</td>\n",
       "      <td>0.332793</td>\n",
       "      <td>0.332261</td>\n",
       "      <td>0.331738</td>\n",
       "      <td>0.331224</td>\n",
       "      <td>0.330717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.007757</td>\n",
       "      <td>0.008242</td>\n",
       "      <td>0.008630</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.008734</td>\n",
       "      <td>0.008657</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.007969</td>\n",
       "      <td>0.007289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333309</td>\n",
       "      <td>0.333285</td>\n",
       "      <td>0.333261</td>\n",
       "      <td>0.333189</td>\n",
       "      <td>0.333117</td>\n",
       "      <td>0.333045</td>\n",
       "      <td>0.332973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333329</td>\n",
       "      <td>0.333252</td>\n",
       "      <td>0.333170</td>\n",
       "      <td>0.333089</td>\n",
       "      <td>0.332846</td>\n",
       "      <td>0.332605</td>\n",
       "      <td>0.332365</td>\n",
       "      <td>0.332127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333321</td>\n",
       "      <td>0.333207</td>\n",
       "      <td>0.330901</td>\n",
       "      <td>0.328639</td>\n",
       "      <td>0.326530</td>\n",
       "      <td>0.320964</td>\n",
       "      <td>0.316285</td>\n",
       "      <td>0.312280</td>\n",
       "      <td>0.308774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019555</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>0.017658</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>0.015205</td>\n",
       "      <td>0.014230</td>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.012016</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.009372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333268</td>\n",
       "      <td>0.332686</td>\n",
       "      <td>0.322389</td>\n",
       "      <td>0.314400</td>\n",
       "      <td>0.308178</td>\n",
       "      <td>0.295191</td>\n",
       "      <td>0.286609</td>\n",
       "      <td>0.280245</td>\n",
       "      <td>0.275239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.004205</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.002424</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.001498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0  0.333333  0.333332  0.333324  0.333152  0.332972  0.332793  0.332261   \n",
       "1  0.333333  0.333333  0.333332  0.333309  0.333285  0.333261  0.333189   \n",
       "2  0.333333  0.333333  0.333329  0.333252  0.333170  0.333089  0.332846   \n",
       "3  0.333333  0.333321  0.333207  0.330901  0.328639  0.326530  0.320964   \n",
       "4  0.333333  0.333268  0.332686  0.322389  0.314400  0.308178  0.295191   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  cINT1_3600s  \\\n",
       "0  0.331738  0.331224  0.330717  ...     0.007016     0.007757     0.008242   \n",
       "1  0.333117  0.333045  0.332973  ...     0.000559     0.000569     0.000566   \n",
       "2  0.332605  0.332365  0.332127  ...     0.000133     0.000130     0.000127   \n",
       "3  0.316285  0.312280  0.308774  ...     0.019555     0.018597     0.017658   \n",
       "4  0.286609  0.280245  0.275239  ...     0.004789     0.004205     0.003766   \n",
       "\n",
       "   cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  \\\n",
       "0     0.008630     0.008775     0.008734     0.008657     0.008323   \n",
       "1     0.000553     0.000549     0.000545     0.000539     0.000527   \n",
       "2     0.000124     0.000120     0.000117     0.000115     0.000110   \n",
       "3     0.016342     0.015205     0.014230     0.013390     0.012016   \n",
       "4     0.003280     0.002925     0.002643     0.002424     0.002082   \n",
       "\n",
       "   cINT1_10800s  cINT1_14400s  \n",
       "0      0.007969      0.007289  \n",
       "1      0.000517      0.000500  \n",
       "2      0.000105      0.000098  \n",
       "3      0.010945      0.009372  \n",
       "4      0.001837      0.001498  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>(93, 254)</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "         Mechanism\n",
       "1933581  (93, 254)\n",
       "1933582  (93, 254)\n",
       "1933583  (93, 254)\n",
       "1933584  (93, 254)\n",
       "1933585  (93, 254)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df = df.iloc[:, -1:]\n",
    "y_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mechanism\n",
      "0                1\n",
      "1                1\n",
      "2                1\n",
      "3                1\n",
      "4                1\n",
      "...            ...\n",
      "1933581         62\n",
      "1933582         62\n",
      "1933583         62\n",
      "1933584         62\n",
      "1933585         62\n",
      "\n",
      "[1933586 rows x 1 columns]\n",
      "{'(105, 197)': 1, '(105, 228)': 2, '(105, 229)': 3, '(108, 138)': 4, '(108, 170)': 5, '(108, 192)': 6, '(108, 224)': 7, '(108, 246)': 8, '(108, 278)': 9, '(108, 300)': 10, '(108, 332)': 11, '(109, 138)': 12, '(109, 139)': 13, '(109, 170)': 14, '(109, 171)': 15, '(109, 192)': 16, '(109, 193)': 17, '(109, 224)': 18, '(109, 225)': 19, '(93, 255)': 20, '(93, 262)': 21, '(93, 263)': 22, '(93, 308)': 23, '(93, 309)': 24, '(93, 316)': 25, '(93, 317)': 26, '(97, 128)': 27, '(97, 166)': 28, '(97, 167)': 29, '(97, 181)': 30, '(97, 220)': 31, '(97, 221)': 32, '(72, 204)': 33, '(72, 205)': 34, '(72, 212)': 35, '(72, 213)': 36, '(73, 150)': 37, '(73, 151)': 38, '(73, 158)': 39, '(73, 159)': 40, '(73, 204)': 41, '(73, 205)': 42, '(73, 212)': 43, '(73, 213)': 44, '(76, 146)': 45, '(76, 147)': 46, '(76, 154)': 47, '(76, 155)': 48, '(76, 200)': 49, '(76, 201)': 50, '(76, 208)': 51, '(76, 209)': 52, '(76, 254)': 53, '(76, 255)': 54, '(76, 262)': 55, '(76, 263)': 56, '(76, 308)': 57, '(76, 309)': 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150)': 115, '(51, 151)': 116, '(51, 158)': 117, '(51, 159)': 118, '(52, 146)': 119, '(52, 147)': 120, '(52, 154)': 121, '(52, 155)': 122, '(52, 254)': 123, '(52, 255)': 124, '(52, 262)': 125, '(52, 263)': 126, '(53, 146)': 127, '(53, 147)': 128, '(53, 154)': 129, '(53, 155)': 130, '(53, 254)': 131, '(53, 255)': 132, '(53, 262)': 133, '(53, 263)': 134, '(55, 128)': 135, '(55, 166)': 136, '(55, 167)': 137, '(55, 338)': 138, '(55, 386)': 139, '(57, 126)': 140, '(57, 162)': 141, '(57, 163)': 142, '(57, 232)': 143, '(57, 270)': 144, '(57, 271)': 145, '(57, 334)': 146, '(57, 382)': 147, '(57, 466)': 148, '(57, 514)': 149, '(58, 142)': 150, '(58, 174)': 151, '(59, 142)': 152, '(59, 143)': 153, '(59, 174)': 154, '(59, 175)': 155, '(60, 138)': 156, '(60, 170)': 157, '(60, 246)': 158, '(60, 278)': 159, '(61, 138)': 160, '(61, 139)': 161, '(61, 170)': 162, '(61, 171)': 163, '(61, 246)': 164, '(77, 316)': 165, '(77, 317)': 166, '(88, 150)': 167, '(88, 151)': 168, '(88, 158)': 169, '(88, 159)': 170, '(88, 204)': 171, '(88, 205)': 172, '(88, 212)': 173, '(88, 213)': 174, '(89, 150)': 175, '(89, 151)': 176, '(89, 158)': 177, '(89, 159)': 178, '(89, 204)': 179, '(89, 205)': 180, '(89, 212)': 181, '(89, 213)': 182, '(92, 146)': 183, '(92, 147)': 184, '(92, 154)': 185, '(92, 155)': 186, '(92, 200)': 187, '(72, 159)': 188, '(33, 139)': 189, '(33, 170)': 190, '(33, 171)': 191, '(33, 192)': 192, '(33, 193)': 193, '(33, 224)': 194, '(33, 225)': 195, '(37, 126)': 196, '(37, 162)': 197, '(37, 163)': 198, '(37, 179)': 199, '(37, 216)': 200, '(37, 217)': 201, '(37, 334)': 202, '(37, 382)': 203, '(37, 400)': 204, '(37, 448)': 205, '(40, 138)': 206, '(40, 170)': 207, '(40, 192)': 208, '(40, 224)': 209, '(41, 138)': 210, '(41, 139)': 211, '(41, 170)': 212, '(41, 171)': 213, '(41, 192)': 214, '(41, 193)': 215, '(41, 224)': 216, '(41, 225)': 217, '(42, 150)': 218, '(42, 151)': 219, '(42, 158)': 220, '(42, 159)': 221, '(43, 150)': 222, '(43, 151)': 223, '(43, 158)': 224, '(43, 159)': 225, '(44, 146)': 226, '(44, 147)': 227, '(44, 154)': 228, '(44, 155)': 229, '(44, 254)': 230, '(44, 255)': 231, '(44, 262)': 232, '(44, 263)': 233, '(45, 146)': 234, '(117, 179)': 235, '(117, 216)': 236, '(0, 146)': 237, '(0, 147)': 238, '(0, 154)': 239, '(0, 155)': 240, '(1, 146)': 241, '(1, 147)': 242, '(1, 154)': 243, '(1, 155)': 244, '(4, 146)': 245, '(4, 147)': 246, '(4, 154)': 247, '(4, 155)': 248, '(5, 146)': 249, '(5, 147)': 250, '(5, 154)': 251, '(5, 155)': 252, '(7, 126)': 253, '(7, 162)': 254, '(7, 163)': 255, '(7, 334)': 256, '(7, 382)': 257, '(8, 138)': 258, '(8, 170)': 259, '(9, 138)': 260, '(9, 139)': 261, '(9, 170)': 262, '(9, 171)': 263, '(11, 126)': 264, '(11, 162)': 265, '(11, 163)': 266, '(11, 334)': 267, '(11, 382)': 268, '(12, 138)': 269, '(12, 170)': 270, '(13, 138)': 271, '(13, 139)': 272, '(13, 170)': 273, '(13, 171)': 274, '(16, 146)': 275, '(16, 147)': 276, '(16, 154)': 277, '(16, 155)': 278, '(16, 200)': 279, '(16, 201)': 280, '(16, 208)': 281, '(16, 209)': 282, '(17, 146)': 283, '(17, 147)': 284, '(17, 154)': 285, '(17, 155)': 286, '(17, 200)': 287, '(17, 201)': 288, '(17, 208)': 289, '(17, 209)': 290, '(24, 146)': 291, '(24, 147)': 292, '(24, 154)': 293, '(24, 155)': 294, '(24, 200)': 295, '(24, 201)': 296, '(24, 208)': 297, '(24, 209)': 298, '(25, 146)': 299, '(25, 147)': 300, '(25, 154)': 301, '(25, 155)': 302, '(25, 200)': 303, '(25, 201)': 304, '(25, 208)': 305, '(25, 209)': 306, '(29, 126)': 307, '(29, 162)': 308, '(29, 163)': 309, '(29, 179)': 310, '(29, 216)': 311, '(29, 217)': 312, '(29, 334)': 313, '(29, 382)': 314, '(29, 400)': 315, '(29, 448)': 316, '(32, 138)': 317, '(32, 170)': 318, '(32, 192)': 319, '(32, 224)': 320, '(33, 138)': 321, '(109, 246)': 322, '(109, 247)': 323, '(109, 278)': 324, '(109, 279)': 325, '(109, 300)': 326, '(109, 301)': 327, '(117, 514)': 328, '(117, 532)': 329, '(117, 580)': 330, '(120, 142)': 331, '(120, 174)': 332, '(120, 196)': 333, '(120, 228)': 334, '(121, 142)': 335, '(121, 143)': 336, '(121, 174)': 337, '(121, 175)': 338, '(121, 196)': 339, '(121, 197)': 340, '(121, 228)': 341, '(121, 229)': 342, '(77, 146)': 343, '(77, 147)': 344, '(77, 154)': 345, '(77, 155)': 346, '(77, 200)': 347, '(77, 201)': 348, '(77, 208)': 349, '(77, 209)': 350, '(77, 254)': 351, '(77, 255)': 352, '(77, 262)': 353, '(77, 263)': 354, '(77, 308)': 355, '(77, 309)': 356, '(125, 333)': 357, '(117, 217)': 358, '(117, 232)': 359, '(117, 270)': 360, '(117, 271)': 361, '(117, 285)': 362, '(117, 324)': 363, '(117, 325)': 364, '(117, 334)': 365, '(117, 382)': 366, '(117, 400)': 367, '(117, 448)': 368, '(117, 466)': 369, '(97, 338)': 370, '(97, 386)': 371, '(97, 404)': 372, '(97, 452)': 373, '(101, 126)': 374, '(101, 162)': 375, '(101, 163)': 376, '(101, 179)': 377, '(101, 216)': 378, '(101, 217)': 379, '(101, 232)': 380, '(101, 270)': 381, '(101, 271)': 382, '(101, 285)': 383, '(101, 324)': 384, '(101, 325)': 385, '(101, 334)': 386, '(101, 382)': 387, '(101, 400)': 388, '(101, 448)': 389, '(101, 466)': 390, '(101, 514)': 391, '(101, 532)': 392, '(101, 580)': 393, '(104, 142)': 394, '(104, 174)': 395, '(104, 196)': 396, '(104, 228)': 397, '(105, 142)': 398, '(105, 143)': 399, '(105, 174)': 400, '(105, 175)': 401, '(105, 196)': 402, '(124, 138)': 403, '(124, 170)': 404, '(124, 192)': 405, '(124, 224)': 406, '(124, 246)': 407, '(124, 278)': 408, '(124, 300)': 409, '(124, 332)': 410, '(125, 138)': 411, '(125, 139)': 412, '(125, 170)': 413, '(125, 171)': 414, '(125, 192)': 415, '(125, 193)': 416, '(125, 224)': 417, '(61, 247)': 418, '(61, 278)': 419, '(61, 279)': 420, '(63, 128)': 421, '(63, 166)': 422, '(63, 167)': 423, '(63, 338)': 424, '(63, 386)': 425, '(65, 126)': 426, '(65, 162)': 427, '(65, 163)': 428, '(65, 232)': 429, '(65, 270)': 430, '(65, 271)': 431, '(65, 334)': 432, '(65, 382)': 433, '(65, 466)': 434, '(65, 514)': 435, '(66, 142)': 436, '(66, 174)': 437, '(67, 142)': 438, '(67, 143)': 439, '(67, 174)': 440, '(67, 175)': 441, '(68, 138)': 442, '(68, 170)': 443, '(68, 246)': 444, '(68, 278)': 445, '(69, 138)': 446, '(69, 139)': 447, '(69, 170)': 448, '(69, 171)': 449, '(69, 246)': 450, '(69, 247)': 451, '(69, 278)': 452, '(69, 279)': 453, '(72, 150)': 454, '(72, 151)': 455, '(72, 158)': 456}\n"
     ]
    }
   ],
   "source": [
    "# Create a copy of the original DataFrame\n",
    "y_df_factorized = y_df.copy()\n",
    "\n",
    "# Create a dictionary to store the mapping of original labels to sequential labels\n",
    "mechanism_dic = {}\n",
    "\n",
    "# Use factorize to get sequential labels\n",
    "y_df_factorized['Mechanism'], unique_labels = pd.factorize(y_df_factorized['Mechanism'])\n",
    "\n",
    "# Increment by 1 to match your requirement\n",
    "y_df_factorized['Mechanism'] += 1\n",
    "\n",
    "# Store the mapping in the dictionary\n",
    "mechanism_dic = dict(zip(unique_labels, range(1, len(unique_labels) + 1)))\n",
    "\n",
    "# Display the DataFrame with sequential labels\n",
    "print(y_df_factorized)\n",
    "\n",
    "# Display the dictionary\n",
    "print(mechanism_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "print(len(mechanism_dic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "unique_mechanism = df['Mechanism'].unique()\n",
    "print(len(unique_mechanism))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2. ML Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2.1 Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.327349</td>\n",
       "      <td>0.293590</td>\n",
       "      <td>0.202480</td>\n",
       "      <td>0.185327</td>\n",
       "      <td>0.176645</td>\n",
       "      <td>0.164088</td>\n",
       "      <td>0.158029</td>\n",
       "      <td>0.154277</td>\n",
       "      <td>0.151664</td>\n",
       "      <td>...</td>\n",
       "      <td>6.275848e-02</td>\n",
       "      <td>6.047909e-02</td>\n",
       "      <td>5.857770e-02</td>\n",
       "      <td>5.622427e-02</td>\n",
       "      <td>5.429247e-02</td>\n",
       "      <td>5.266523e-02</td>\n",
       "      <td>5.125899e-02</td>\n",
       "      <td>4.893444e-02</td>\n",
       "      <td>4.706765e-02</td>\n",
       "      <td>4.419414e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.320007</td>\n",
       "      <td>0.267789</td>\n",
       "      <td>0.181156</td>\n",
       "      <td>0.167704</td>\n",
       "      <td>0.161147</td>\n",
       "      <td>0.152048</td>\n",
       "      <td>0.147856</td>\n",
       "      <td>0.145341</td>\n",
       "      <td>0.143635</td>\n",
       "      <td>...</td>\n",
       "      <td>4.819785e-05</td>\n",
       "      <td>3.918124e-05</td>\n",
       "      <td>3.289082e-05</td>\n",
       "      <td>2.668646e-05</td>\n",
       "      <td>2.236475e-05</td>\n",
       "      <td>1.936135e-05</td>\n",
       "      <td>1.689938e-05</td>\n",
       "      <td>1.375380e-05</td>\n",
       "      <td>1.109475e-05</td>\n",
       "      <td>8.746055e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.323456</td>\n",
       "      <td>0.278272</td>\n",
       "      <td>0.188772</td>\n",
       "      <td>0.173904</td>\n",
       "      <td>0.166546</td>\n",
       "      <td>0.156165</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>0.148334</td>\n",
       "      <td>0.146305</td>\n",
       "      <td>...</td>\n",
       "      <td>4.775315e-07</td>\n",
       "      <td>3.885170e-07</td>\n",
       "      <td>3.280314e-07</td>\n",
       "      <td>2.669785e-07</td>\n",
       "      <td>2.243345e-07</td>\n",
       "      <td>1.943700e-07</td>\n",
       "      <td>1.704890e-07</td>\n",
       "      <td>1.384731e-07</td>\n",
       "      <td>1.158476e-07</td>\n",
       "      <td>8.839853e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333330</td>\n",
       "      <td>0.333262</td>\n",
       "      <td>0.333191</td>\n",
       "      <td>0.333120</td>\n",
       "      <td>0.332907</td>\n",
       "      <td>0.332695</td>\n",
       "      <td>0.332484</td>\n",
       "      <td>0.332274</td>\n",
       "      <td>...</td>\n",
       "      <td>3.818451e-03</td>\n",
       "      <td>4.606387e-03</td>\n",
       "      <td>5.326639e-03</td>\n",
       "      <td>6.277075e-03</td>\n",
       "      <td>7.080558e-03</td>\n",
       "      <td>7.749471e-03</td>\n",
       "      <td>8.297305e-03</td>\n",
       "      <td>9.092941e-03</td>\n",
       "      <td>9.576317e-03</td>\n",
       "      <td>9.959288e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333322</td>\n",
       "      <td>0.333111</td>\n",
       "      <td>0.332889</td>\n",
       "      <td>0.332668</td>\n",
       "      <td>0.332013</td>\n",
       "      <td>0.331366</td>\n",
       "      <td>0.330728</td>\n",
       "      <td>0.330098</td>\n",
       "      <td>...</td>\n",
       "      <td>4.149791e-03</td>\n",
       "      <td>4.047360e-03</td>\n",
       "      <td>3.932019e-03</td>\n",
       "      <td>3.763374e-03</td>\n",
       "      <td>3.611976e-03</td>\n",
       "      <td>3.478599e-03</td>\n",
       "      <td>3.359512e-03</td>\n",
       "      <td>3.156144e-03</td>\n",
       "      <td>2.987952e-03</td>\n",
       "      <td>2.723336e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "1933581  0.333333  0.327349  0.293590  0.202480  0.185327  0.176645  0.164088   \n",
       "1933582  0.333333  0.320007  0.267789  0.181156  0.167704  0.161147  0.152048   \n",
       "1933583  0.333333  0.323456  0.278272  0.188772  0.173904  0.166546  0.156165   \n",
       "1933584  0.333333  0.333333  0.333330  0.333262  0.333191  0.333120  0.332907   \n",
       "1933585  0.333333  0.333332  0.333322  0.333111  0.332889  0.332668  0.332013   \n",
       "\n",
       "         cSM_180s  cSM_240s  cSM_300s  ...   cINT1_2400s   cINT1_3000s  \\\n",
       "1933581  0.158029  0.154277  0.151664  ...  6.275848e-02  6.047909e-02   \n",
       "1933582  0.147856  0.145341  0.143635  ...  4.819785e-05  3.918124e-05   \n",
       "1933583  0.151293  0.148334  0.146305  ...  4.775315e-07  3.885170e-07   \n",
       "1933584  0.332695  0.332484  0.332274  ...  3.818451e-03  4.606387e-03   \n",
       "1933585  0.331366  0.330728  0.330098  ...  4.149791e-03  4.047360e-03   \n",
       "\n",
       "          cINT1_3600s   cINT1_4500s   cINT1_5400s   cINT1_6300s   cINT1_7200s  \\\n",
       "1933581  5.857770e-02  5.622427e-02  5.429247e-02  5.266523e-02  5.125899e-02   \n",
       "1933582  3.289082e-05  2.668646e-05  2.236475e-05  1.936135e-05  1.689938e-05   \n",
       "1933583  3.280314e-07  2.669785e-07  2.243345e-07  1.943700e-07  1.704890e-07   \n",
       "1933584  5.326639e-03  6.277075e-03  7.080558e-03  7.749471e-03  8.297305e-03   \n",
       "1933585  3.932019e-03  3.763374e-03  3.611976e-03  3.478599e-03  3.359512e-03   \n",
       "\n",
       "          cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "1933581  4.893444e-02  4.706765e-02  4.419414e-02  \n",
       "1933582  1.375380e-05  1.109475e-05  8.746055e-06  \n",
       "1933583  1.384731e-07  1.158476e-07  8.839853e-08  \n",
       "1933584  9.092941e-03  9.576317e-03  9.959288e-03  \n",
       "1933585  3.156144e-03  2.987952e-03  2.723336e-03  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism_(0, 146)</th>\n",
       "      <th>Mechanism_(0, 147)</th>\n",
       "      <th>Mechanism_(0, 154)</th>\n",
       "      <th>Mechanism_(0, 155)</th>\n",
       "      <th>Mechanism_(1, 146)</th>\n",
       "      <th>Mechanism_(1, 147)</th>\n",
       "      <th>Mechanism_(1, 154)</th>\n",
       "      <th>Mechanism_(1, 155)</th>\n",
       "      <th>Mechanism_(101, 126)</th>\n",
       "      <th>Mechanism_(101, 162)</th>\n",
       "      <th>...</th>\n",
       "      <th>Mechanism_(97, 128)</th>\n",
       "      <th>Mechanism_(97, 166)</th>\n",
       "      <th>Mechanism_(97, 167)</th>\n",
       "      <th>Mechanism_(97, 181)</th>\n",
       "      <th>Mechanism_(97, 220)</th>\n",
       "      <th>Mechanism_(97, 221)</th>\n",
       "      <th>Mechanism_(97, 338)</th>\n",
       "      <th>Mechanism_(97, 386)</th>\n",
       "      <th>Mechanism_(97, 404)</th>\n",
       "      <th>Mechanism_(97, 452)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 456 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mechanism_(0, 146)  Mechanism_(0, 147)  Mechanism_(0, 154)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(0, 155)  Mechanism_(1, 146)  Mechanism_(1, 147)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(1, 154)  Mechanism_(1, 155)  Mechanism_(101, 126)  \\\n",
       "1933581               False               False                 False   \n",
       "1933582               False               False                 False   \n",
       "1933583               False               False                 False   \n",
       "1933584               False               False                 False   \n",
       "1933585               False               False                 False   \n",
       "\n",
       "         Mechanism_(101, 162)  ...  Mechanism_(97, 128)  Mechanism_(97, 166)  \\\n",
       "1933581                 False  ...                False                False   \n",
       "1933582                 False  ...                False                False   \n",
       "1933583                 False  ...                False                False   \n",
       "1933584                 False  ...                False                False   \n",
       "1933585                 False  ...                False                False   \n",
       "\n",
       "         Mechanism_(97, 167)  Mechanism_(97, 181)  Mechanism_(97, 220)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 221)  Mechanism_(97, 338)  Mechanism_(97, 386)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 404)  Mechanism_(97, 452)  \n",
       "1933581                False                False  \n",
       "1933582                False                False  \n",
       "1933583                False                False  \n",
       "1933584                False                False  \n",
       "1933585                False                False  \n",
       "\n",
       "[5 rows x 456 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df_oh = pd.get_dummies(y_df, columns = ['Mechanism']) \n",
    "y_df_oh.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 456)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df_oh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.where(y_df_oh, 1, 0)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_df, X_test_df, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.05, random_state=37)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data has 1836906 observation with 240 features\n",
      "test data has 96680 observation with 240 features\n"
     ]
    }
   ],
   "source": [
    "print('training data has %d observation with %d features'% X_train_df.shape)\n",
    "print('test data has %d observation with %d features'% X_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>...</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1459100</th>\n",
       "      <td>0.666666</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.561708</td>\n",
       "      <td>0.553785</td>\n",
       "      <td>0.547734</td>\n",
       "      <td>0.540914</td>\n",
       "      <td>0.535847</td>\n",
       "      <td>0.531923</td>\n",
       "      <td>0.528790</td>\n",
       "      <td>0.524104</td>\n",
       "      <td>0.520750</td>\n",
       "      <td>0.516260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896046</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833330</td>\n",
       "      <td>0.833298</td>\n",
       "      <td>0.832626</td>\n",
       "      <td>0.831924</td>\n",
       "      <td>0.831228</td>\n",
       "      <td>0.829172</td>\n",
       "      <td>0.827164</td>\n",
       "      <td>0.825202</td>\n",
       "      <td>0.823285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968160</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333297</td>\n",
       "      <td>0.332970</td>\n",
       "      <td>0.326250</td>\n",
       "      <td>0.319520</td>\n",
       "      <td>0.313114</td>\n",
       "      <td>0.295610</td>\n",
       "      <td>0.280275</td>\n",
       "      <td>0.266704</td>\n",
       "      <td>0.254590</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069588</td>\n",
       "      <td>0.063861</td>\n",
       "      <td>0.058443</td>\n",
       "      <td>0.051457</td>\n",
       "      <td>0.045791</td>\n",
       "      <td>0.041188</td>\n",
       "      <td>0.037404</td>\n",
       "      <td>0.031587</td>\n",
       "      <td>0.027340</td>\n",
       "      <td>0.021568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605448</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000006</td>\n",
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       "      <td>0.000003</td>\n",
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       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
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       "    <tr>\n",
       "      <th>1574931</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666148</td>\n",
       "      <td>0.661630</td>\n",
       "      <td>0.603996</td>\n",
       "      <td>0.578012</td>\n",
       "      <td>0.563949</td>\n",
       "      <td>0.544214</td>\n",
       "      <td>0.535572</td>\n",
       "      <td>0.530625</td>\n",
       "      <td>0.527411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065278</td>\n",
       "      <td>0.065304</td>\n",
       "      <td>0.065319</td>\n",
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       "      <td>0.065355</td>\n",
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       "      <td>0.065369</td>\n",
       "      <td>0.065375</td>\n",
       "      <td>0.065381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 240 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "1459100  0.666666  0.000009  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.833333  0.833330  0.833298  0.832626  0.831924  0.831228  0.829172   \n",
       "968160   0.333333  0.333297  0.332970  0.326250  0.319520  0.313114  0.295610   \n",
       "605448   0.333333  0.003058  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.666667  0.666148  0.661630  0.603996  0.578012  0.563949  0.544214   \n",
       "\n",
       "         cSM_180s  cSM_240s  cSM_300s  ...  cINT1_2400s  cINT1_3000s  \\\n",
       "1459100  0.000000  0.000000  0.000000  ...     0.561708     0.553785   \n",
       "1896046  0.827164  0.825202  0.823285  ...     0.000009     0.000009   \n",
       "968160   0.280275  0.266704  0.254590  ...     0.069588     0.063861   \n",
       "605448   0.000000  0.000000  0.000000  ...     0.000006     0.000006   \n",
       "1574931  0.535572  0.530625  0.527411  ...     0.065278     0.065304   \n",
       "\n",
       "         cINT1_3600s  cINT1_4500s  cINT1_5400s  cINT1_6300s  cINT1_7200s  \\\n",
       "1459100     0.547734     0.540914     0.535847     0.531923     0.528790   \n",
       "1896046     0.000008     0.000008     0.000008     0.000008     0.000008   \n",
       "968160      0.058443     0.051457     0.045791     0.041188     0.037404   \n",
       "605448      0.000004     0.000003     0.000003     0.000003     0.000002   \n",
       "1574931     0.065319     0.065338     0.065347     0.065355     0.065362   \n",
       "\n",
       "         cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "1459100     0.524104      0.520750      0.516260  \n",
       "1896046     0.000007      0.000007      0.000007  \n",
       "968160      0.031587      0.027340      0.021568  \n",
       "605448      0.000002      0.000001      0.000001  \n",
       "1574931     0.065369      0.065375      0.065381  \n",
       "\n",
       "[5 rows x 240 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.66666375e-01, 8.64659390e-06, 0.00000000e+00, ...,\n",
       "        5.24103567e-01, 5.20749569e-01, 5.16260321e-01],\n",
       "       [8.33333333e-01, 8.33329783e-01, 8.33297833e-01, ...,\n",
       "        7.39333964e-06, 7.19593722e-06, 6.89835126e-06],\n",
       "       [3.33333333e-01, 3.33296993e-01, 3.32970356e-01, ...,\n",
       "        3.15867918e-02, 2.73400487e-02, 2.15682561e-02],\n",
       "       ...,\n",
       "       [3.33333333e-01, 3.33324903e-01, 3.33249064e-01, ...,\n",
       "        8.90973969e-02, 9.32020928e-02, 9.88450912e-02],\n",
       "       [3.33333333e-01, 3.33322224e-01, 3.33222288e-01, ...,\n",
       "        1.10472690e-01, 1.14783402e-01, 1.20264515e-01],\n",
       "       [3.33333333e-01, 3.31398102e-01, 3.18951029e-01, ...,\n",
       "        7.26743944e-06, 6.06035491e-06, 4.55024782e-06]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train_df.to_numpy()\n",
    "X_test = X_test_df.to_numpy()\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[412 245 128 ...  62 136 400]\n"
     ]
    }
   ],
   "source": [
    "# Find the indices of '1' in each row\n",
    "indices_train = np.argmax(y_train, axis=1)\n",
    "\n",
    "# Create a 1D array with values corresponding to the positions of '1'\n",
    "y_train_1D = indices_train + 1  # Adding 1 to make the index 1-based\n",
    "\n",
    "# Print the result\n",
    "print(y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[306 402  60 ... 131 132 154]\n"
     ]
    }
   ],
   "source": [
    "# Find the indices of '1' in each row\n",
    "indices_test = np.argmax(y_test, axis=1)\n",
    "\n",
    "# Create a 1D array with values corresponding to the positions of '1'\n",
    "y_test_1D = indices_test + 1  # Adding 1 to make the index 1-based\n",
    "\n",
    "# Print the result\n",
    "print(y_test_1D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Train KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "classifier_KNN_40 = KNeighborsClassifier(n_neighbors = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Accuracy Scores: [0.02907872 0.02931562 0.02841464 0.02923668 0.02915502]\n",
      "Mean CV Accuracy: 0.02904013594675577\n"
     ]
    }
   ],
   "source": [
    "# Perform cross-validation\n",
    "cv_scores_40 = cross_val_score(classifier_KNN_40, X_train, y_train, cv=5)\n",
    "\n",
    "# Print cross-validation scores\n",
    "print(\"Cross-Validation Accuracy Scores:\", cv_scores_40)\n",
    "print(\"Mean CV Accuracy:\", cv_scores_40.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=40)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=40)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_KNN_40.fit(X_train, y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_predict_1D_40 = classifier_KNN_40.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2858916011584609"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_KNN_40.score(X_test,y_test_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data\n",
    "plt.scatter(y_test_1D, KNN_predict_1D_40, marker='o', s = 1, label='Data Points')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_test_predicted')\n",
    "plt.title('KNN_40')\n",
    "\n",
    "# # Adding a legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KNN_model_conc_normalized_40.joblib']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(classifier_KNN_40, 'KNN_model_conc_normalized_40.joblib', compress=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba_KNN_40 = classifier_KNN_40.predict_proba(X_test)\n",
    "print(y_predicted_proba_KNN_40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_predicted_grouping(predictions):\n",
    "    threshold = 0.95\n",
    "    index = np.argsort(predictions) + 1\n",
    "    prob = 0\n",
    "    grouping = []\n",
    "    probabilities = []\n",
    "    for j in index[::-1]:\n",
    "        prob += predictions[j - 1]\n",
    "        grouping.append(j)\n",
    "        probabilities.append(predictions[j - 1])\n",
    "        if prob >= threshold:\n",
    "            break\n",
    "    # print(grouping)\n",
    "    # print(probabilities)\n",
    "    return grouping, probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba_KNN_40 = classifier_KNN_40.predict_proba(X_test)\n",
    "print(y_predicted_proba_KNN_40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage: 93.63570541994207\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "total_iterations = len(y_predicted_proba_KNN_40)\n",
    "for idx_proba, i in enumerate(y_predicted_proba_KNN_40):\n",
    "    # print(idx_proba)\n",
    "    # print(y_test_1D[idx_proba])\n",
    "    grouping, probabilities = generate_predicted_grouping(i)\n",
    "    if y_test_1D[idx_proba] in grouping:\n",
    "        cnt += 1\n",
    "\n",
    "percentage = (cnt / total_iterations) * 100\n",
    "print(\"Percentage:\", percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_KNN_40 = load(\"KNN_model_conc_normalized_40.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_predict_1D_40 = classifier_KNN_40.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gHHaOE4Y0rxGSyquMi6zHDkDf+3Og3YR4wjHZbuLEZyzCYJcfUiz8jVlYeDZsuOuuu3DJJZcAAC677DIMDAwcsnNt3749c6S///3v973fqhDsEgKoDtMtLLuFaG3SijsWIIS3q/eaVtYMdHeUZdDbISk+X6p6xy2svs+EsARUhuhm632EoIiaj5921o1SlfapDs0q6igG1gC1IeDAY9DNOgQEkCpoSACCJyALiQyQi7Ys5SgyQN954bI33PN9ZDT4702Y7xrDMIeOqOJsjnVik8IirC3xKdXI7tZGZpfWHIRkkyqDeZs5k33LA3SrDrlsk+qQcdptaUcyu0j3aiRt522MLbECoJWG+um1wPAIPefPO3gOwDAAON0649JLL0W73caxxx6Ler2OL3/5yx3b3HHHHdn9733ve9i1axcA4OUvf/mMnOpNmzZh/fr12Lt3b99K1+12G5dffjkA4LDDDsOLX/zivs+37IibQKuRrSJrgC7aRpBL9EqTttv5zrJdzbbP517vYQhsWpJfr1WqUb2zfx45izUmKckYtROKvlnhLeE5p/6YkzZNCMKSczK0omOIPgRW0tiIsxihMZVS/bEwddXj+6BLR7h0u3KNU9kWEkElAiSgFixdJ9kK2eV+MyadUoj+okZRmb7/StF7bddp/1KFsxcY5lBRtFP2t2av82lMt6g8d00EIWe/9iUkEBTHKqj0RxZTr+MupVdMBzKATpOs/IzmGSBb325QhDkMgZEN0PUxZOVr7CQzDAB2kjNaLVpFu//++/F7v/d7027/gQ98ILv/wAMPzDjyPFNBsG9961vYv38/AOC1r30tgmCF1ow2J4HJg5nB1H5KdLthUql9p1LBrZL6jmvh8/XTQ7N/e/wNwjKQJIDyRL6iqjuHPfdM64G1JsOUmBXbbJUX3sq4oOPabW3qdWqEUJTKO+uAi0oXSdpu2xSec1143+0mvSajmYmtMPODlIBcgqUPljRxQnZauyhTUHI1yv2mZwYREABojLnvvFad2RQMwxwagpAizHZhNCcmuUSv/0EERNp1l7DdHWaTFr6KoACDgk5iF1EWgE4VsPshoDpIAm5hCWJonXOOWSCtLwQWNh13Cf4yVzzsJC8Cjz/+OPbu3QsA2Lx5c1/7rIpU6+YkUB916UDm8pNpb5Uq0EK6BQZhHEo/klUU0/IFuzrosVqatI1QV+AcYpuCbaO+QsxcuCtuOgfZksaegqcdU8FBzrZN8qrbQE/xMd2chIibudV2nbRJ3Gx4Ax1LJcDIYZR+F4TA8PqlG8lkFg//ey6Eac9iJ1F91CJ3HK+wwDTT3xHDMLNHCOdc+rXJSzl6aK871kkG+LoxDVqZ2vOoTMEFOw/QGiIIILZugz7iiVD33g55zFMgwpCCAwCnszOMgXPcDJ/73OegtZ7y5ot5XXfdddnzRx999IzO9alPfSoTmTrrrLOm3X7fvn34j//4DwDAU5/6VDz1qU+d0fmWDTkHUlOkNefIFhxdrQHIQkr1DL7Svba1zmnRsS5GjmeaIlp0emXQ3U+PKp110TLsO3KtD+wCDu526VVwomYAZTGIkU0QR5wAMbIRYv1miLWHQ/TTo5lZffgiPEXhuJli1bx9ZtHCjGGYecD/7c2mN/JCYh1lesDRzinQSkFd/g9Q9/03PdY611FDmLmEkAHkttMgqgP5+cxKzVScZ6QQC35jFhZ2kueRBx98ELfffvuU23zrW9/CX/7lXwIAqtUq3vCGN0x73C996UuIY3KwVmwUGXApzYFRyw0iUmG2K8aiR7qJVvk0oepIvsejkJ4z6h0hmkLdWph2T9bRjIyImO+ozrRNUodzYR0Ok/otJDnDsosjImX3KG+3icKB3S46nWtZ5d2375FhitgeqpMH3ffICujMtW2T7ZMMABD0m+fJLsMcetpNytRqTrjfYBAaocyhpb9YJQTZ9eqQE/ZiurN3J7DjV8DjO6Hr46ZNXwJdH4O+9Tr4q/PCr1mPyu7GMAynW88nDz74IJ7//Ofj2c9+Nl7+8pfjqU99KjZt2gSAap2/+tWv4qtf/WoW1fvwhz+MI444Ytrj2lTrMAzx2te+9tC9gcWmVAGSarbiKQDoUpXUl7Xu7iB3i/ZKCYiIHG2lTEq23cCIcEnZ28jaFO5sF9MLdmAt1VJmz88wJSmqGDEuEzFXCQBFaqJNG10zqdblAaCtXfQ5aTt1bJWQjQuC7u+hXKVo3b6d0CMbACGhRx+H3v0w5MlnmHR1U9/lLyYwDGDE4hJ3PxONm4dV7KKIEEcsGObQo1LP7qQk5uh3WFhO1Y68uDs9Q2spzfpJvwYEIXS7DUgBPTkKrF1LNcrlKmzkgf76ws17Zqq3skqRWNhII3/zFx52kg8BN998M26++eaer9dqNVx66aXYvn37tMe66667cOuttwIAXvSiF+Gwww6bt3EuOVr1fC2MEJ0CZ1JO0zfZbJ+0vDriwjbT9XTNerh2Ef+SoXMgZhpRE4KcjaTtP2kcB7+tjjFWMiikaJtt5TSRt01HAbsfNC00SFBObNxqVIR95e9lNDFiVga2rUvSpt8Cp/gzzALQQ8iSWZGI6gDE77wlEx8VAXVOEJUB6CccDVF1Qom6W203fz8YBgA7yfPKM57xDPzbv/0bbr75Ztx222147LHHsHfvXiRJgrVr1+Kkk07CC1/4QrzxjW/MIszT8YUvfCG7v6JTrQGTAtalB2tuG3hKXvZ/dnvpVsd7OtJ9XPyzlWrjKAcllxJaqhrxLD279DQreqSSfFuncs2IkgjXYzIsZS2bsvY5/bDnIUqrCyMIbHTvpDpIKXeVAZNCPsPxa03jFpL2T82CxlRR+bmiFEXsZWhSxkxqvQDc31LPPH1cpfljyYD2t1F6SxC6bQFv8qBdKyMriiLDlTG5yL53CSm9z7eYWxCyQBzDLCRSkl2JjSDlaitx8O2IFd1MvYwsIemxgJk7mAXt2VzPMzsZTG+ztaJrrQzMfikpTytSoxalKglrCkCnKQUSbJmYSgFIY+srQLUKtOnvq1sN4JE76T0cdSJEdZAyGBsTnXbS2m6/W8FM+1wzzApFaD1VWI5ZCYyNjWFkZASjo6MYHh5e7OH0Jk0onbnY69gijDPm1zQCzvj59ZJKAa0J0xbKi/6Wav1Fkm3dVlhedinJ+t6fuve77gkQtWEysPVRAIA47JjCDlMpgHvbNCc8NW2R377Uo7bUr+u2j4Xob/KRJp76qgC1+/KOa511O6mpDvbnrPutsfzjZXlndqzWKVed763Yc1urzu/gcqKf7wDDMMxyQyVOLFBIKmVqTiDrB6xVZ0aLdVyrw9PbRSBv3/z2drURZ5M6tlVAfQzFrDUdt4F9j9CDgTW0uG2OoevjJGiqNS14aEXOb20YQkjodguiMQadtIHRvbTdYUdDHH4M9UqO29Cjj0MMr4coVei45YElcd1fNvNUuLG+r7wGlQX87Jpa4y9bB5fFZ7RS4OV8ZukQhFT3C7h+wlqTA9Yr6ll0VixSUv2uv429Px3WOPa7/VJjzUZg/2N0X4ZkRCcP0OPaSH7buOUi2OVa7wifXR13T+Sj+rZ21SeNqa2Xxf9bRZWpxUHiFk1krCOcnc8ey1sN99t09eUk2xR24fbVCuSEB958ReTT3f3vkX0Mb1vbd3Smgm6Lje29bSeLpery/N4zDMMUib3yJq0om8qmCwmJrhlIQjiRwV7X8zQx9k1TBltUIXvh24y4RXbVt4WlGkWDk9iNwzNuIipBj2wCxvaSc19zzpCoDUGr1HQcEEBzkpxdU1cuSi1gcA2tQx95IvS+ndB7HwUOP8a9//07geGN0y8AMAzDTjKzxLAXbRuZtLW5/ewz1fOzqR9erqzfAgyuo5puEwXXpSooLViS4ZWSlIWzvpNmZbqXk2x7UXfrQ21TjYu0vZ6Wxc8zbrq/bzdakyYLwF8EsY6yzO9nHdd+U75lAKTemITwsvy1m7fYaIDXa7qv79pyIW65+nj7d01jQJeXn6PPMAzjY69vRZuVtoxj7BZJbUIlJRIJV15TzLPURlgzK8sxG7SbeWfcYkUQ/cXVdh2IG8i6ctgsJmvrABMwWANAQasUQgbQQlAfjKhMvY6FgK4MQGhlXGwNlKvQXvtIsX4z9PotJpssAZSCePJzsnkBMzdYuGvlw04yszSxIlfMjBFCZIJdpGrahpABICOnop0qQNfzO+rUpZl1HpQi882JvKq3jMi4K1MvFUQunb2bI+ujkt7CTUICSF10uDLoot62bsx3XEtTRMGL2Ai235c7W9D3IgfFvtb+e+n23paLo6yNsnls3n9x3MvlfTAMw1jHVStaXJeBUfBudt8W+Wt3ZplUAm07adismuKlMI0Lwpv+sa1uhbeYrFWuSigbg6kzdsriClqZkaQJOcBBAIA0L1SrAVkdJGfY2BsruGX7fmhQRpQQAjpz/DVEWAKEpH8HV1ktOsPMEfZCGGalYuuILWEhvdlXEs+2n6QelN0cJSmp7rfdJOMfVWjC4DuTaQyImknbrdGquUrdqrt/3FadnN9uTnl1yKWy2W2kEUJJ2sjVRAfhzITD7AKMrTmWdqKi6TNKY/P+yiROFkb5uvapxN+Wg4MZt5yDDNDf3bZ4MhMqhmGYZUGrTnYGoOva4Lp8CyOrX6GVK5lRvogXuZlaSIg0gRbCpTMXr/Vpkn9ss4wSvyzHaqfofPtJe12NTbq3BrVnsuOzIl8ygG7Vabym7EtGFajGOEQQQivldFJyQ3SRaCGEp2s6w1aVDMNksJPMMCuVohOsku7b5bBq4b3SiqUz0M3J7sdUihbApSRH2nfUi1HYXpFrGZCjbBXP7X6lqlPe1IrUOpM23Xo53EXaLSCuI3O07cdk66QTSRMvIYHQTFzSuHf9u30cVZaHg1mcNMlwyYi3MAzDzAjfBmmjYm1Vo4VEVmaUqq7XONtmUoig97U9O752x7ULxaUKML7XK1HRQGWYbIiOvSwq47gHQeZsa60gZGjkPTzxLikBFWTPaa0gKgNkla2GRodytnBmW2sICDpXeWCaD5CZLRICcgF7jC+D2cWKg51khlktCAkE0ov82kLcrCDXRRSno0PIKztJPoW6uPLeMZ4e6dZJiyYZ9pgydK2rsmPbftbeuaZzklt1L826sBiQtOj47cYUY+7hKJdry6fnb1h2kY8gorGzg8wwzHKkVHUp0GHkKVkLY6NMqZCedLbP17UolAJ5rmqncFepYrKjjK2x7ZiiirMbUdmUNQl3HH/xVDonWZjsKKog0s5RDiIgKEGnCW3jjysTm7SdIqS7n4WPrc6GWSzg0jWGmRX8y2GYlYoMkHOAw4iivGni+mYCbrXdb6s0HVZQzUarbf9nWViND0Igy0QTQHUAmSr2VOdqF+rJVNJp7IsO8XQTAa3zdcgdr6N7HVvxGN3GHLeWj5MchKSYOpO/N8MwzFJEhuSY2r732fMBOcfSKFhXh037v4m8KCNgHE2bYeMLOnbpKTy0wTjJXh/lyiDZU5V6i6zapV0XFcD8bCiAIr+e055ZbRlA26rjXPp3oQ2m2S/DT/GeSSkSMyOk0VtbsPMt3KkYAzvJDLNSEYJqiNPEGcvWGL1mBL1QqhrnsU2TjKiLsnH2uqnPtc5gecClIfsTBp/A9A5OTJ1vmlAkczrHLBM/8bbr1eYrjWdQl+wtGhSRJJLSs4bLTkK6plwvM/MlZF8JAwzDMEuauNFZWgSYTCVviqtSigLb67gyQl9p6iLO2b5eFLh4obQ1yz4qzes8WIrK2tmxPRtkhbjaTaOVQQ6zyNkY4yBnjjQ69s/G6g+5m2AmwzB9w04yw/RDaiOZUZdaoCWG1kBqxK0C49SmSW9VzsQTckrjzv6JaeIirGlMTi/gUnZlQBMOleSdVa2dCFZqzm1TtHv1vbZUBoCWnfxocsKtUqh12IOQHO6p+i37CEF1zu2mqVuDcW6tM25UurMUNrOPn2Ltt6LKUt5A+2q1/JxlhmGYlYTtfVzy2hwlMUWQ7es2EmttyWyu29a+KWPfsi4H5lhFBzk7RyHancakVC1lFg3XMqCWTp5PnHOa7SKy9uuszb9SQitlnu6R+cTMC9wCauXDTjLDTEeaUN9egJzJXurPS4WWV3tl1Yqtg+wLmVjnUhVXu4u1unE+eprG3VfNAUqtLg+QA9uuF84Ldz47MenZd9hEvuOGO2fSzq+QNybob1HpX5hEC1OT3ZiAkFbN2ahmtyecQxzaVhlePVn2r3CRC+tMq9QsMCzx7wbDMMxKIqoa59Jru6QVLf6Wqnn7DYAistJElpt07Q/8kiNjA5IEqE5xLY+b7rjWhmhNfZizU3nlR7qQ4g1TMy0EBAIzBpCTnCbGClM7J6gYOpUQpr2iNinZ0AraSkdJt7+tcUbSBmqCk4YYZpawk8ww05ETn9JLXwjDH69NtbZoBdRG8ttHZedUB1F++7jlosB2AtEtta14/iDMj6PoZKcxnacy2N2pbE4AjUkSRikKlQD0b1Sm8c3AScbY4zQxypxZm6Jm2j/ZiY4fQfYjAllUucuabrayzzVgDMMwC4KUdD0HgMkD7vk0BlDtFI8UAOLEOa0KgNT5doZRmbZLEyox6oavX6FTUJyvkEYtiunaxn4OrKX922ZsprexMHbH/gshXHeIygByC9gmc0nIgPZVnuK1bW8Yt8x74P7IDDMblvBMn2GWCEHoBJ+sYNVSJow8tU/jZNrIbzdxKRmYfoxdnL9uEWMZdk48fKxTGZbcREIGnelnWlGUOupiwBsT+bR2YdKZbQq0dn0m+0UrRcqnNl2cDuy9Ly9SnDsvvFRr//lCvZcViGEYhmEWBpWSI2kXKK3zqwFM7PfEJE1Utj4K4TmNWmvgwC6gNuSsQRID5WrnAq7WJlMr6czAsmKTsqicrbzyHdD/6qMF++E7v+55nSZmLDVki7lmoV5DQ8jQO4IG2k0IaxOtuNhyEZRchrBw18qHnWSGmQ4rPqUU9R1c6um0pRoQlMjmWiMqQ2S1vd3IKWd6yID6S+aeM7XAaWzSzESh3tkY+ahCn53SLm26SC9hkSCi44dePXCpZurKTAQ4CGe2Qi6EiRYH5tQ6/6fMaspEdye4+NifCMmgd1ScYRiGOTS0G16qdUrlPlqRpoWt3Q1K5Lw2J7LIcBa51SkwMAKEJWiVQtgsJ3RZEI9bZOuKitU20wkgp9bqWoSePodvL7wFY23tbtKGtubDnjeJgSSGDlPIyPezJdBuQosg21YA0EFIjnUak51e+4SCABjDMDOBnWSG6QcZLP0IskWIzhSxmaaHJzFNHqJyJi4CgJxMeK2UtAJEcaXaS4kOIkpl6+Uk97Lfg2tddECGLu26VHPiX7btVJ8IALo6kBfbskjjFNt0a5glYg36LDXyNW8+MqCJ2XKejNi/dxAtn+85wzBMsfxHhk6AEUDWRzhuZnZctxuumwMCiEz8MYKOTe1vt2u6TvOLpbarg9+y0Lakyvo0A7kFamtLYRzkNKHXrc3Wio6nUsqogqbocObUm/MHIW1XqprjU5xRRCXS3ijXSPyLOWQIwFaEL9D5+O+50LCTzDBMniQm0S0AgGkTZZ3iuEGr8j4d0WDPaKi0izBYj219pKRJQ9yiiUm7i5OdxrSdnEYpu3i2LGrupbjZOuqszZNRv4YA0ikMk9ZufOXa8nOUtabPOG6atPw2i48xDLN8yNoF2sdePa9FBoCKIUztL2QIlEtAEndEWq04FtqTQDBSKLPxE149hzWMXLeHIDC2QLr2VFYozO7nOzuZc2x7JUvjPJuIdHUIiEpkrWzEWmtyxKMyWTBz7Oz9BQFEYqLeA2v4es4ws4RT3BlmpeKngM0EVRQqK9YfT3NMa5DjFkWDM4e7gAx6i6I0J+k21fi1ph6X/WJT4Ghn5Bx0P9JuI8m96ov9MfliZK1pxrsUaTdc3bhNIezW25NhGGYpUqoiu5ZHVacbYmtxhQRKZZMFBQhJYlcCIIdWK+fAZmnU1hktXAujMnLq1MW2T9Y5b4wB9YN0zupwfqHYa9lk+yFnStVaQ6sEolyBGFwLDI4A5arrm2xsmPB7KdubOQc9L10t9lT6IQzDTAlHkhlmJaIU0Bx39VgziXIW021lCMBr5RSVXYoYBFCq5B1E6/j26stcqhoV7SnG066b45mxdKsR7jbWqeg26bHHtq8Xx+Q/LtaVFcek0u7HWMokcf6xYPExhmGWEUHoOjb43Q/KtbzOhI3uTh50zrGQxr4ZJ9O/dMuw81ooJEVmtabjWJQiOwjkndLWJNyisj24JnXrpOWEMa2TG9eNSJd9b93KiTQtyurIpZL79sgfczGizswrLNy18mEnmWFWIknLOYRpG9AV9N2aqJgebVtsKOVWp6tD5BTaxxX72HOycqJfkuYIQk7vIANOGTttu9X4NAZahaj0dO2oiuRS87z2T7YdlH3vMvBSr73PQ8O9v25p5sttQhIEblInQ2ozstzeA8Mwq5te16zi881x51SmKSAU2Rfbvs92MghCoNxFiFFr6pCQtMkOpAnZNauNYZ1UrZxNcTubMQVOryNu5Z3cUpWUq+3ir7k265IR50oVkJjMn+akycYqm8VNgaz1U+DZetaYYJhZw04yw6xEOiYNM3B8ukVThQSCwgq1n6JcfAyYWmbjbCYtJ37VnCBRFDnFumh12KXLlUwKXVii99GqAzCTiqmO0e19VYfo/L5zLQL38ZSqAMxEw044wojOq1JyJO2EKiy5aLkMSM17KTmYSdv1yOwl3FYe6GwXxjAMsxxRypT3CJPxZDowpAmgYmT1wKlZ0C3XnC2wzq0MgFKPxcK4mS3Uaq2BdgM6TSDWbKJ6YRhXOG5l4l0mmRpq1w4S2nrC0cDYAxBRGbrVhBgcNuc355NGGyOJqRymOoTMQAUB4GdPK+X28xd1tUm3tgJizCFBYmGjuxxJXnjYSWaYlUhYNqvliamjmsHlNSzRvio1jtMsL83CTFS0csJfAD1uTRrjX0CldAuifNpZNrYIEAM0CZGSHFNt0s+U8qLCwsyHNOVDKSDrV1kaoMlOVmut3IJ/3DA9o0G9LKGBJHWfQdp2k5HYUzRFMLPP+FBilVxtKl9ixLi6RRTs34hhGGa5M3nQXdd9LY245cphIpMWXR2i6zngpSqbxdC4QQuIRawjrDX05CjVHicxMDACbcuMGpPA/keBNYdTaynrw978H8CO+4F1myDOPBtq7ADw6AMQZ/62c6iFMMlKlHmlgzBvV4oOb1Qm4Ug/u0l6UeV2gxZIuYSGYWYFO8kMsxKxqVuz3rd/xWhX69tFVRQwdcyVvFOpfYfWkMYU5bX7VIe7r4JnEwcrkjKer3/OjUG4CZBKAduKo1TtIkjmjY3uTP++s1X8JSJ2pTVFUuIWcunfNlWeYRhmpWFTlu01vaglIQN3ja4OuvRoKQFVN68V9CW6UapCtxpAawKiXAHKFeiJg9AHdkGsewLtl7SohWHapt7HYQQ068C69cCBvcDYAej9jwN3/YSG95PrIJ5+Fi0KS6+hkAwhZMEGaYWsdMlGiTOBTvM+iw5x1vKQmW+4Jnnlw04ywzCzJ02MOIlBCKAy2GmoI1M3ZZWuu6X2+iJSNqW5Wx11u+GcYj9duBvF9h3aOMrZY+Pc2+d8sZYg9ERY7Db+tt7Eawb9mg8ZWlP02+8RCphUeb7UMwyzAkliUpIGzPXbPG+FuQCjNWFKggKvo0LOsQ6MfYCLNhfQKgUm9tH1XgZAZRByYA30xCj0/seoJdPACERYglYp9Ph+CGjo/XuAw7YCh22FGBuFfvxRd9DhtRC1IdNUwbNJQWiu257gZKkKDK6jx/Ux9x7tuIvecDenmWGYvjkkM6fJyUk8+OCDGBsbQxzH0+9gOPPMMw/FcBiGOVQUHVRbS9UthTeMgGDIrewXCUKXlp1r11Q8vnfOuEWTnrTHdcZGe4XwJhLmXGEpP5Gy962DWR70BFiEt60VWvEEWhZ7ImIFZXIK25omftz3mGGYlUjcNGUxACAo1GZ6BQulTB2yISqTCrZ/LcxlN6UkQClk19IZ3aoDj9xNEeKh9fl07IFh4OAeiOoghF0wFVTzrO79OcRhWyDSAWDiAHRtAHjsYRM9LkNsfbI9A72H6jDZtczOGfsVRi47TEh6Lyo1i8ZeCyu/tnkmXS0Yhulg3pzk8fFx/MM//AO+/OUv46677iJRgxkghECScD83hllWdHMOp3IYheyd+mXrn1Vqao97behHc434StJ2DqKdLEE4tVKtjaq2pGNbJ92PVBej1kK456wIli/cleuZucjYGmu7IJAmNKGqdFFoZRiGWe7YzBn3hHna+1cp6iVcqnZfLMzZKuG6NXRj94NuEXdsP1CuAkFI50kTIG5ATxzI2lEJIaB/+kPIJ59M4wlM94BWG/KMc4BKFXryIKlZZ45t4G5FZGG6nrQ7F4d1CmjzPkpLTEhyBSIhIBcwl30hz8UQ8+Ik//jHP8Zv//ZvY/fu3QAwYweZYZhlSlQmnzVNAJjV7rmk9lqRkSQmQ188lhA00YhbyHo29xKfShMziTA1WSKYnYJzalRG7X3/+Es1SluuUaRjKY6NYRhmvpEBZf+0Jsg+xG1yYqtDEL3ECW3nBK1m1p3AZhelsbFXJupreygDgNbQOx+GOPEU6nesEmDsIOTxTycHG6Co8+jjwMgmiFLFiVWGJZBopGkxFUT5kp40zpc5hZV8ppRWpKRd6SI+xjBM38zZSX700Ufxohe9CGNjY9lzpVIJxx13HNatW4cw5Fo4hlmx2NZMNko7V6dMayPeZSYa5Vq+hgwwk6Euytc+vgiYzv7nlJ5nQrFvtBvszI4z39gWVbb9VJpQND2qzEx4jWEYZrkhBEVtmxMu/VgGQDBCjqdZUBVW5TlNOp3GmQhcHnY08Nh9dI0dWke2JGlThFdKYNPRkFVzfK2hVQJsOQaorYEwraBSGUFWatk2KFehgwAijQHpLWoKQdfxgunLRDLTgrBYGAKyknecl4qY5AqGhbtWPnP2YC+55BKMjY1BCIHDDjsMH/7wh3HeeeehUuFJGsOseHzhriDs3jZjJihPWAugyUDRSe53XBYvO7tn7fJUhBFNhjKRFE+sa7EitUlM7UcA+nyqQzN3/hmGYZYzUaVDZEvIAFh7OD2oj7rIbq9uBn0iyjXgyBOhR/dSiyiLVhDlQYgynBMrBIQMEZz+fLMN6UMEJz7TLGxKO1iIUpVe70f8sTnpta2Szia1m522qIf4GMMw/TNnJ/k73/kOACCKIlx77bV48pOfPM0eDMOsGHwRrTTJC2HNBlsTZic2s03dDqJCH2P7/CxUqIWk2t5M3Rp0fzHFumKv73Qa91YCZxiGWam0m0CrbqLKw2Q/lKKUa5WaDgTWlsxisdVDt5vA2ONGQduzUVE5L5hlxR1pL/NcwYFtN2lstreyTfmeCqWcgww4Ya52o3MBICy7YzMMM2vmJd1aCIEXvOAF7CAzzGpDSiDL/DJ1Wt1IE3JatXJiWmGps5ZYCFIYTRM6ti9gojUdI43NvlOslAchqYTaWml4k5j6mBP8EpKO26o74SvbiqNcdZOsuEXHCiOX5hxG9LzWdLyoPOeJWN8EAZB1p1oC6toMwzALidYui8lew0tVUvm3Zkil0KUq0GqYlkmaIsJxy7MjxgalCR0D6H49H98HpAlEEEHLCICGfnwn5GFbcl0UdNym+uAw8pdUs3GKrGZZ5fV7hJFlUik50YBJBddAY6KLE61NS8UuNjdpkT1jZ/mQMsWM55Cdj1lY5uwkr127Frt378aRRx45H+NhGGY5EZaRtUfqlX7sT2b85+Kmc4KTNhBE0GmbUokrA5Q255MT0EpM/VkErRSlHquUJhpakxMpZH5VXymXbm0d+8ogHdOOxU52lJkwVYecgw8AbdtTU9D9LJXPTNKqwwuTgh1VAAh6z6xiyjDMKkdrBRzcTc5pEAIQ0ALkHFvnN25CjxwGkXjXcysQ2Zzwrv9drucm2qvjJiBDqPt/CRzcAxy2JStM1QB0Y5zOH0ZmPViabJ+UbJpKyTnWmsYWhq6VYRCSrbQ6GC3Tx1mZcVqfOiyRXbJjs+0MkzaNXUqnUREM8SIqw8ySOTvJxx13HHbv3o19+/bNx3gYhllOCEGGXZnoqo3EauW105hC4CpNKTUOgE7GnDNaH4NevwXC71dZVM23jw/u9lKrBVAqAWkXZexsHGZiYSci9jh2wuGpk9K/PdLguqn4+2l3hxIhWJyLYZjVixC0iNmqk9M5vh+kCN2Crg6R/UliiiL7FK/naZKrJc5v613PhzcCY49Dpwn0vbcDY/uA6iDUo/dDHHMitZrSGqJSg544CJEmQLlGjjoAISV00gZs3+OkbRaLjd5F2gbSEjm5MNZKkaMrimOxx/CRIaAaziH27RmvoR4SWLhr5TNnJ/n888/HTTfdhB/84AdIkoTVrBlmNZHGbpW+iAhITVRIijgnrS4beY6mKrSwUAkgvRrisEQr7iohB9imwvn1ubZW2NaM5SYSvsMunEJ2qWJW4D1BFfu8Pa/tw2xr3gA6fxojm4GEpfz+DMMwzKEjLEGr1DnIlrhF/YLTdt4GlAcohbmVZplHaI7Ta0GYtxmF67kII6iwCv2DLwK1AYgNR0AcfyqEEFATB4EBV94j1x0OHbecbRQCOqpQJDpNKNvJ2qlShVpBAZlSt9agsSgFEQTQWkP4tiuN8++rMW7aL0b5xVvbdYJhmFkx51/PhRdeiM2bN2Pv3r3467/+6/kYE8Mwy4VkCrVonbpV+1KFUtfCQvTTr6f1679sW6nitrVhYGBtPg2uOuhtIzsVst2AAAhydAdGXKRZBtTfcmANHbc6TO1E7MTFRixqI+Zfs01lwD1XHe6/lQjDMAwzZ/T4PmBsr7EhLsqqdz2YF7kCgNoIxMhGyk6qDgO1NfnX04SeszagcD3XD94J/cn3Anf+Atj9GMTGrdlrcnAN1H99Eeon34MolaEbE8DY/kJWkqYIs11wtRTtnFb0emMcIgiyWmcSnewRtvTtqNX7qA4b3Q0OIx8qJMSC35iFZc5h34GBAVx11VX4jd/4DVx88cXQWuPd7343R5QZZjUgg2naKgknihWW84JTdv+BNSSIIgOqK0sTICpT+lrXQxaeH1pPjqpKqf4LGoCkJUAtXABZgMaRxLTyLqWJEid5Z946x0mb0rhF4CYbWpn0Pe2EXXgSwjAMs6BorYDxvUBYhghLFKmVJMQo1m/ORLEEAAytI9sQt+j6bvsm+0jPIe2Cuv5rblF458PQo/soYqw1UB+n1yYOQrcbpsbY69QgJaAFRGUQOmlRfbLWpmuCfxLjIAchCUcaSNArQWbI/DEWI8Vak+1Kk7w9YxhmxsyLJ/vMZz4Tt9xyC173utfh4osvxj/90z/h5S9/OU466SSMjIz0nuwWeP3rXz8fw2EYZqGwYl12ZVzrvNOctF2bqHajiyK1SW8LKU1MJEZsxE4U+kBobURKEppDlKs0SYlTJ3YijQ5lGsMJeaWFVG0zRnveLI1ckbNcqpLqqG23sZBCXQzDMIyHiZ4mLXKYywMQUZnuRyWIiQPQB/cCW7eZml5zHTd1ywDIXpXMAmg0jQDi4Ih7vTYIcfiR1BbKLp5OTkD82plUYxyEZHvaDVpItecoVSCCEPrgbmD9EYAUVLssJe2TJkAY0rquDKGhyUTSYL23Lt3CrlZkm1TqOkfYGut2g2qVuQyIYWbFvIV7y+UyTj75ZPzkJz/Brl278JnPfGZG+wsh2ElmmOWGTe2yEeDi6nxH2nNRfMu7H7ecCnbcAgbX9FdPZZWtrWJo2xNqCSOj9Omvvk8jJtYrU7vni0sIu0ghg3z7LIZhmBWEEAJ6/RZqzRSEwPAG0/7JOIlRGeKIJ3WKXvmpztZ+9dEmSb7ktVDXVYFWE+IpzzTaFAmNQwYQZ74CYqAQGS5VKBMpS2UCIAOIDVs8f1wbPQzfPtnhCvd+coMJgNQTIJMh2co07iIoucRt1jKGhbtWPvPiJH/3u9/Feeedh0ajkUWNdccPlWGYFYlW1MfRGmO7yh2WKSU5TU26dYmesw61bXlh8ScvduIQ9GEWcpOewmui0Mkwp2AtnCiXJSq7Vfeo4lpD2V6aUQVQdbd6v5SiyFrTJNFGGCoDC9e3mWEYZoERpSqw9gmuJrdcg2jV6RpeHaKN/LmoDEzrJErL7rBBU51rcATyJa8FxvdCCGlKg1LQ1VYB1VpWewzAZFlJcqK1ghWVFIEn/ggga1WozLjSxImI0RsojMREwmVoFLklEDfcsfxF4LDMi6UMMwfm7CTfeeedeMUrXoFWy6UtHnnkkTj55JOxdu1ark1mmJVOUSgrLDmnEsgLawGkMNqNqOxaOQVR/8a9VHXRY2VaT/np39DoqOHy06R7CW6VKp1tlmTgJl9LDa3y7U2SmJ1khmFWLpOjJvtIAEPryIYICUwe6KzFjSr5BVEhaCGxT3RjHNi/k+5HFYioDJU2gNG9VHoThNBrD4cIhJc6XTKnktRfOap4rQdp2LlsKSld3+a4lU+TDqK8GJkQQHUEaE56g1T0HKdXLwgCCxvdXUJL8quGOXuwf/3Xf41WqwUhBLZt24bPfOYzeM5znjMfY2NWMnELaDeghYQefZxaOAythzj8mL5r2JklQtGZne3KdRACg2udo9vv96Bco8mRMsVbMjAOo1/DBVevZVtvrDT81lcAO8hLHa1pgq8SUn1fSX2vkzYtXAlBi2IzuSYkbaBdd9ke5drMxYe0pgW3JKbrylLL+ugD6v27D6iPUk1rdQBCpeRosZK+EVG0DqKm9klR2Xx3lEuxFgKIqkBUMqJWJqAje09/9eQoMHkQeueD0D++Bvsem0TtzN9A7UnH0vwkbkJHZfq7WO2KNKH76zcD9TFzJPreaiGAwfUQ7UK7xPIALejaMiVrn4LItLHyFniDyOhueC0IAeOQG+d5pdo2hlkk5uwkf+973wMA1Go1XHPNNdi8efOcB8WsUJQxalqbi70GVBsiKtGE4MBjVIc6tG6xR8rMBCGByhDVQ1nBkrkcq58U6yIyyC/pilWYYmZbVSWxi0YwS5e45UTu4oapn18B31vr/AO0MNVudKr49rOvPUCrPnMVeZW6qGEaA2m4fFR+rbOlFfXwLVVpkc86Uu2G6eG7Ar4rc0LkBaz8ln6ASUkOqE2fdSgjm36sey4i6qRNCxNCAJuPBp5wNNYc/Dn0IKVTaw2IqAQxvAFo1fPJ0CObgKEN1AEhbtLrpQrE2sPp7+c7ydUhWgCyjrsyqdMRpWlDSuq8IExkOgiBMDTXd1l4T5L2D7nbAsPMJ3OeRe3ZswdCCLzwhS9kB5mZmsaYm7gEoRNbCiKIDUdA731kccfHzB4pAVmefjvm0CJkPtWdOTQo5QTSVstihG0tI8T0TqufxTETfZLEFx4SM8svVKlr0bOc/YTJg87xM1H4juwqm22zmhGC2v81J/M1yKUaLc6ohO77zrDtpDATBkYQnvAU6J9cA/zWhWjv3Y/yiU8DghCiNgwceRL02F6IoXXkDAPQ6zZTFoAQwOA6kxkl3SJmqeKu01ZZu0gQdTryva7vQQSs8q/DYlBQPFmQ8zELy5yt+/r167F7924cfvjh8zEeZiXjCywp5VobCAEdhMCaTZ31qwzDMEsJrWjBz8aQKoOzS22PyjSRVwlNkpe609OccJHvsEz1nPaa7o/diuVZR69YH2nb0xRTQ5M20Bilff19bI/yqVCp+ZtkgzDiR4rSapdL6YH9bGBa25l0Ya11ZisZjzCi7DOfGdYad6ABtW8nxOBaYHIM8qnPgZASuj4BNBsoHf0kCM/RFms2QazZlB9CEAJrDuscV7kG8Bomwywb5uwkb9u2Dbt378auXbvmYzzMSiaI3KQqCKF1TBMBABCCVsuT9vJJi2MYZvVRFKqzSu0zRYj+05CXAsX+5+3ApUaXatSfHMg5erSftziqNaUQWztQHSYH27Z/y5xt42RXhvuL/HW0njPHqA73++6WDlbdGMicYuFrDVilZOaQoCf2A7/6GbD5OMhtz6An0wSiNgg9OAIZrZLMEWZapBCQC7hwJTmWvODM+dd+/vnn4/vf/z6+//3vY3JyEgMDc1jBY1Y2/iQrjaEnD9JqLWDaH/RIO2JmRhLThFMAkJHpnahALY/MY3uxDUteb0Uz8ZIhTd7jpmuBVBmk41r1aTsJtq00/LqwylDviIc2NYbKTARLU0SJtAImDtB5o7JJbw1JBCWJqTavVKF3EpaAgTX9RVrSGBjbB+iU6tVKNVMrr+i4QphWVSl9Fkmbxl0ZoDE3J50wjB/xiipLP9U5aVG9nHXQlKK/hwCldq6W1OG5IEMWSAPy/cjjhnOSbTp2FnX2Fj1Vms8oStpUcxu77hjZMUq1/lNju31vZ5pWuxSwfXvtdbY0QtedNKY6WSBfj8rkSdre9WzQZRM0JozNiaCbdSCuQ+/dCb37YaBUgXzy6XS9j9v02ZerEEc92R1XBtBK0WKFvcar1NmCUpWe15rqjpO2s6NT2cJ2nRZFpBGXixu0rzJtEgfWAlJC2xr1VoNqlgfWQrQmnW0SAGprzHsdB4XDU7qmL6eFOIZZYsxZvfzCCy/EU57yFIyNjeEd73jHfIyJWan4apLmvtp1P9TjO6AP7AbWHUFpSszcaE8C0GQ807YX1dFeCwwzwfdbYlhU4hxkgIxt23tsVWdz/YdNr8c0ddv5pAlNKKyar32uawQI0GkMfXAPtFI0adQK2oiZCCEgohLVhNkdkjaNcTqSNjC21wnHTR6EjpvQduJuhU906j4LgJ5rTpJjkC0QGEfJOktxMx9BW2rYBQqYRY123QjJaDdhY6ZHmAhleYD+XS3XLP/6bYWEur0GUO1ldQioDedVu4up1zZy3C3VfCYZRTKgv0WpRrfK4PLNSBpaT7fhjUBtiD6zqEy1rbVh+pfTrrtjFzuVdz1rtzybE9MCfRID1QHIJz0NYuvxEFKahRoNRGXIk55NoltaQWsNLQOIgRFaiLXfq5ZnC9oNI0TqCcZZOwqQ09uYIAc2aRtbWHf2TyV0fruvVeVujOZtTtksKLXqZO9UauqdAyqHaEwYp1kCQYn27WFjGYaZnjlb93K5jKuuugpnn302PvWpT6HRaOBv//ZvsXHjxvkYH7OSqA0BrRCABko1iNoIsO8R6DSBWM8O8vxh+x31SVdhneIkTOS3LU7SrMMoZefxOhRr/ddSAF2iIgd3A0JChBXPKS84oHYiYRVOp5s4ag1M7Hd9Ku37MhMTbcYtihN2e1whnNic//xSRinjvOvO8apCmyxO5eofIZevEzZbqoM0Mfczfmw0udi+ykZEi1gl/KRtRM/Mb79co2tHmprjz0K9WQZLv667H4To3uKpuDDBEFZQLnc902ZhdoL+9a5/+sAeiKHhTP9ElGxKvgRKZQitgEABlRrQqEOs2ej6LwOUbZTG+YwIO47iImma0G8kiZ2jrtL8tdiMWSdNdwW2Y9WgcwlprLqmbiDWcfYylzRgVK41hB1rEGFGcwFmRrBw18pnzlfcv/zLvwQAvPSlL8U//uM/4gtf+AIuv/xyPPe5z8XJJ5+MkZGRvvvevu9975vrcJiljJA5QQ0BABuP5B/+fFMeMJNXTZPGNCVn1BpOXz0WnvPkO0xSUkQmadEqdalCbVSy/qVdyH7nxb/oLIy0SoHACNZk46MIMzm2MaXOhZE5mwCCPibIdrJkV9sH19F7UioTY9Fak6OcJnkH2X8r2Vv13mup5j7jxcK2VxOmz3RjLD9xs06E1t5igaZIYLm24MNllhFCdn5HZvOd6ebs9XIMGWY62k0XNc6cYQ2dktq5AKCFpGixVkBQXCxMoZIEYmAIkCGEENAqJbuz/oi8XUnjvECc35veX0DNjp24rKVsn6K9NYuzgGkDBc/uCaNkTluJNAGgIXJrm3R+aye1XTx2G/T3OTIM04HQuteMtz+kSX/0yU1sZ0CaptNvxMyYsbExjIyMYHR0FMPDy1DIhJk5aUICOQA5RuVBAJpW1i1BREY+KdQDagVE1c7oEGDqnScAFA2xR1By9YmWdrPzPACthHepQ9fNSWB0N1AdhggCcpDtceImjb0yQBOf7Fh91AQ3TcqbkFTvFZWgTfqa8CPTlQFKiVNJ3vEtD9AkyW7nP7/YUZ5MFMmm5tXyf2/A1IuW8voAtlXKUnWSu0XBGYZhAGqZ5S9oJm1oCOc0muuHEAK6OQk9to/aag0M0WJiWALCECIs5x1YZWuCPSe5NVko6zFK7DbLCKDtdSFbqRhhzhas/dIdmIQpnZX/WPumTRRamH21kBCKFr61sTtW/TzLiLLvZSnYpj5YTvNUO9bPjWxAbQEXxuta4cLRvcviM1opzMtfV2udu3V7brrbUubP//zPqQ7S3K6//vpp97n66qtx7rnnYsuWLSiXy9iyZQvOPfdcXH311X2fN0kSfOITn8Bzn/tcbNy4EdVqFccddxze9KY34Ze//OUc3hGz4km9WmOVUm1TYyzv2KnURIq95+xvsZvoTbZiX0jnlsVtu/2ee/zGdXfnR1QGgI1HeePQENAQpTIwshGiNpR3kIHOushuVAaBoQ00MWpNAO2GmWz4k6OUhlsdAgLP6bZRAxm6z8ymjy6FSYhW+VTyJOkSsZP574YVdFuKgmPaLOo0xujfJW4nGIZZDAo9uUc2AdVh0rPwntbNSWBgBOIJx0JsOgoQEUSpQjoXRcc2TaCV7pIZVLBX5RqJP+ZSrwWJaAVeuYGQtFBt1da1cvXEWfmO2VaarCjfvsmQjheWnXBgGNHYu6V554a8yNlNDLOMmfPM7v3vf/98jGPJ8rOf/Qx/93d/1/f2Sils374dn/3sZ3PPP/roo3j00Ufxta99DW984xvxyU9+ErI4yffYu3cvzjnnHNx666255++//3586lOfwuc//3l87GMfwxvf+MaZvSFmdSBDAMXIrVnttivmNrKrlTG+AQBNjlXRsGpViASbFLPKUKfgUzHqp1V3gTBzmK6kKUTTT2tzGwobNfBTp7UCVDx9ynUWCTcTk3aD6uMBN1nRpo43CAuLDTFFl+2+WgNQFF0oD3SKEi00QiK3gBGGQFA16X5mm5aNLGuTRl+lqPxij70bft2fSilatBwVixmGOXSEZSCddCU0jXEAAkjb0AkArWhRtbQGgM10BHTNdGHQClorQKcQWkPbEqSkTWnX/kJjGOVV3a3Qowxy7S2zmnw/YyduIitvsrYrLNPCplK5a7C1dll1j5TuWSEg/C20Minl2hzeF5T0ulYw8w7XJK982EmeAuvwJkmCTZs2Yc+ePdPu8xd/8ReZg/y0pz0N73rXu3Dcccfhvvvuw//9v/8Xt99+Oz7zmc9g48aN+NCHPtT1GGma4txzz80c5PPOOw9/8Ad/gHXr1uGWW27BBz/4QezZswdvetObcMQRR+Dss8+evzfNrAzCEsgpTpyzYR3NtN29hchUDkhXbS/RXdG5KJ4zlY3uJbTTHCeFUtnDLGid/xeYfsVca3ISi2JVidciK3svwihB+5vKzvdqHeV2I1dvvygIQWI0SUyfaxgZ4RjTxiuqIOdEy2BpRpAt3cThGIZhfKKKy6JRCaBSusrZqK0QEChkLFrdCYAWCxVpNGjhRY+7lRsFEUWD05hsrD1GdYhUp31BvzACMOAUr23HBAg6tt02KpGtSWJk12a/zlgjV2udQ0hAU0mQyJxi33UTnIHDMHNgCYYPlg4f/ehHceutt+KEE07ARRddNO3299xzDz784Q8DAE499VTcdNNNOP/88/HMZz4T559/Pm688UaceuqpAIC//du/xb333tv1OJ///Odx4403AgDe8pa34Morr8RLXvISnHbaafijP/oj3HTTTRgeHoZSCm9729uQJCzxz3QhjChSWB7sdEaLDqXtPdzToBaER6zDogrfvbDU6YAXfRshndM2XZpy1+EUn9TmWDONMhqnUsC00TARgbDcv3Kx8Fb0lwLSiKyFEf0tJw+QA9+cMI78IH1OYZe68aWGDOnv2u93hWGY1UfSIqdV5zVthFZkqqyD3Jx0StfZRgWxSa+el25dpshhRGnW/vVISNcr2V/MCyPjEBeOIUNnc4RR5bJdCAAXWdYAbIu+ftsLWmc509jgxUWGmS3sJPfg4Ycfxnvf+14AwCc+8QmUStNPmv/+7/8+c1gvu+wyVKv5SWitVsNll10GgOqNL7300q7HsY72unXr8Ld/+7cdrz/xiU/Eu9/9bgDAvffei6uuuqrPd8WsSqSpm7U1u0HUWb8bm3rj1mR3R7nYsqLbNlGFJgodRtmkldl0uFKVnLWpopjlgalzi6RElkpmRaemmwwIYRSojTNcGzafReBW6q3KbhA4QTF/oiECt8BgnfIloMyrlcrV4AHobPGkTI2y7SXri8eolP5dSlEHYWqlp/uuzBWr9N3REothmCVP0vbqeoVLMC6qSKcxkCYUQZYBOc5aQ5iosojKdE23zmhUnb4MxV47/ae0hk6TfOS6ZEQRNeiY1tbGbVNKYhao08Q4yMbOBF5bs65SH6ZsphfhLFqpMX3jaxUt1I1ZWNhJ7sFb3/pWTExM4IILLsBZZ5017fZaa3z9618HAJxwwgk4/fTTu253+umnY9u2bQCAr3/96x2iZffccw/uvPNOAMCrX/1q1GrdFWcvvPDC7D47ycy0CEHpwLURo2LcLW0Y3dtY0AHIwSqkrOUo9o0EyOA3xsgJF5K2aU2a+qwpCCMSP7G1WFnLIhvB9lLX+l1hB9wkxP4L5CcRWlHUVSmKAJS9FGoKS7iIhY2iz2SV/xCgG+PAvkeo53jT9KNWKUWR/ZlVqUppfZMHqF900qZU7IkDVMfXGJ+6xddKJGkDdSNq1xijaNNqev8Ms9zJLQQqqI+/F+qqT7uXAeiH7obe9XAuQ0gAQLtJbQWN8yGyyK7ubN1UJG4C+3YC+3eSwjZoHoiDu4C9O4C9O6BTrzeyMq0Y/WO2JoDRPU6zQ3Vp1WjH1M1ht8ravT6XpbbwyTDLjAXLX2u1Wvj4xz+Ob3zjG9i9ezc2bdqEc845B29729s6Iq6LzRVXXIFvfetbWLduXRbVnY4HHngAO3fuBIBpneqzzjoLd999Nx599FE8+OCDOOaYY7LXbJr1dMc5/PDDcfzxx+Oee+7BTTfd1NcYGSYj5xjqwqp7j0hyqQaoibwz7O+bmtpeP0XNX+X3Hcm4RdFlgJzSVgNaGoc3ialfpZ+6ZmuvVFqYLGhyDMf2mih1hY5drgIQJPJlHe3csUwdcRjRGH2BlSCium1Z6d4ey34ePnFrFune88TkqLtfH6Woa2Pc1LWZOraqiZo3xt227Qb10Pb/Xmli/oYrJPqQtOl9CmHKDgoTzeJijalp5NRuhlkmROWs1Z3e/Qgt/Cmdv0YnMfSPvgukCmLb0+k5rQBoCKWdHTORZSvcRZlRXa6F7QZQHzOCXQnQGIeuDJKD7Du8zQm6lthrEFCwmXahVVFmU1QGmnVk6dKWro6wcPbaOva5/fTKu54vMVi4a+Uz55nAgw8+iDPPPBMA8LKXvQz/+I//2LFNs9nEC17wAtxyyy3Zc/fccw9uvPFGfP7zn8f111+PTZs2zXUo88LBgwfxx3/8xwCAv/mbv8GGDRv62u9//ud/svsnnHDClNv6r9955505J3mmx7nnnnuwY8cOTE5OYmBgkYWDmOWDDChK2jKRx2y1WfQWwMp6A3tOcrF1hgY5lra+WfRYxbbtlOI2Re9MKwsNARGWSEQljel+roasy7H89kBJmyYbdsPAqI6KwHOSBZBqt1DQLR3Nb/GELhFyID/ZWcw2G0HoJmZBaBxhT5xLJV5quCc+pm3dW2FhY6WkdGntfb8BxI18ZgBg3nePHqYMwyx5NCTUrd8HHt8JvfcxyBe+ktKulULWHxka4tdfDgQhdJrQJS4xC6Nh6Ik3wmVMiR62sDlhuhwgK93RWgP7Hy1kU2kgbkFPHgTCEoRRwtYqISfepGoLm1attalVll5GlyjYPC9y7NdgS+mVQRXFvVbI9ZxhFoE5O8lXXXUVHnnkEQgh8Fu/9Vtdt3n/+9+PH/3oR3SxKqR+3HXXXXjNa16Da665Zq5DmRfe9a53YdeuXTjjjDP6EuuyPPLII9n9LVu2TLnt1q1bs/s7duyY83G01njkkUeyNO5ejI2N5R6Xy2WUy0tY3ZY5tAQh1Xu2m85RKgqPTEcxzThp56NzGtkKPQA38Sh3LyPIoZRLM9OKehYLmU+DK7Y96peo5MZg64nTGJniqHUqS1UgFmbyIyjyrFKTam0+p0w5epEY3kBRDV/gxa+jrq1xkdHaMNCYcL2goanPtQB9zmF5dTmJ5ZqJPJnPLCotzXZYDMN0oCcn0PyHv4R48H8QrB1A+Kb3Aus3ARDQj9wL1CeglYI87iSae4aRqes0egeaHFk6mInIBiAbUNDX0FoD4/toEbJUpau/ENAigN6/k56zLaEAsiF2kS5uQtvnbfcBe42OImfXWnW3eJ2V88AcU+TTp4PIPJe69wCjj5Gz53w9Y5jZMudfzw9/+EMAJEr1ghe8oOP1iYkJfPzjH88Kzt/4xjfiG9/4Bj72sY9lUdrrrrsO3/ve9+Y6lDlzww034DOf+QzCMMQnPvGJGRXJj4+7NMbBwcEpt/UjvhMTE7nX5us43di6dStGRkay2yWXXDLtPswKRwZUq1wdon+nVZuexhntVptb3Ed4q95hROfVyCYROolpXhAaR7YyQLdSxdUJl2uUUh2VgaENNO6wRDXXcQuZ05x2qfEKwvwESAg6Xm2EPodcmrcRHStVaQx28lQZogWGUtX1nV4kRBBClKsQKoXIRLjMey3V8krdMqDIia1lEwKomr9/uY+//3JCCCMAJ41SdpeyHrtgUx0033/uw8wwywGtNcbf9kY0v301mo+OIkkrwMg6EuYSlJGk/+srwIHHTW9kT61aGIGtotK1VaMu1zozjEb3AAd3kyPbol7JGgJ656+A/buAPQ9DZ72SS2bxOV8vjYmD5n6KzEb5TqzN7rHXYX+xs1vGU6lSsK+a7FF5oD97zswJuQg3ZmGZ8y/ozjvvhBACp5xySlcF6G9+85uo1+sQQuANb3gDPvWpT2WvPelJT8KLX/xiAMDll1/e1cleKNrtNrZv3w6tNd7+9rfj5JNPntH+zaaLnk2nhO1HbxuNRu61+TpON3bs2IHh4eGu+zNMXwRBoe2TF8UNTM9Hv99jESuI0qLejqgMkoNWHcpnlbWbtJpeH81PDiqDFK1O2uYcRvGxNkKvTRcFbE66/cuDU/eGBlxNK2CcZS9i3G66muVStf+2UfNNazKryXO5eebfdp3GWB12k62w7KUallZ2pCEsLd7fhWGYQ0ejgfSuXyJ8+qkY/MDfQEQRdKOeOb5qB7XY1Pt2uTrjNIG27fFUCiEldBgZ7QpjZxJSwYYM810TcnoOdaBGQQy59QTog3ugdz8ETIwCRzyRrGK56jJ8KrRYJ9ZshG416PxpClRr+bIdm5EUlui67b+mzBitI66MyGRWGatpv5axBbYlFcMws2bOTvKjjz4KADjuuOO6vn7ttddm9//oj/4o99pv/uZv4thjj8UDDzyAW2+9da5DmRMf+tCHcNddd+HII4/E+9///hnvX6m4yXO73Z5y21bLiQEVRcuKx/Efz+Q43RgeHs45yQwzY9Ik/7gyaIy4ds5WbdhElAVFdX3xq1wrKUUpzmGJUrTthMJ3l60itp3AxC3jpOvC8TQ51NXBfDQwbnqpaMhHupPm9E6yv2/SyjvJ/vtK2ovrJPvYNDs7PttixI4vCGlRwf+bMQzDLCeqVYTPeBbK57yMFm8BiGoN+uA+6DCE2LgZ6ugTIZ93DhA3SMVaa2DkMKBVh6iYchubSZS084ustqwnK1sZoWgyAAyuy9kysWYT9N5HgS3bIGytsBA0Lnvd1QqAuTZPHARGNkJYO6a1yfox9kgGQHXE6Cp4mhtW4CuJvbImTQvNRWHGuMVO8iFmodtQc3X5wjPnGdLkJE3QejlfN9xwAwDgiCOOwFOf+tSO15/ylKdAa42HHnporkOZNXfddVeWenzZZZfNSgBraGgouz9d6rP9zIDOlOr5Og7DHBL8/sq2XrgocGL7EPtpYz2PZ5zfhnWQgQ6FzuL2tj4L8MRKzP1m3W2rVd7JtWPu9l6mGx/QGaXOvedFdDb99yFDYGhdp8NeTNXrJUrDMAyzDBBCYPDD/4jgSduoNlhrSnfWMYRKgbWbEDzvpc6xiCrkeAYBUK7l061t5JgObM+Q94CGNwAbjwLWbaaMKbg8Kq01xPGnQRoboc34UBmiNG+7rdZkR0Y2OAfZksb5RVwhPO0I+5wkG1dcrLb2NmevWNGaYebKnCPJQRAgTdNcVNPy+OOP41e/+hWEEHjuc5/bdX+rau3X4i40l156KdrtNo499ljU63V8+ctf7tjmjjvuyO5/73vfw65duwAAL3/5yzEwMJAT2fLFt7rhi3X5Il4AOo4zlbq2PY4QYlqRL4aZF0oVFzkO+1ilDiMANUotU0l+ElCqeoa8IL7li5NUBoDEtHKy6bNx0x0rjYGU2nlMv6wrnJhJP+MvVYFE0tCiguNZGSB1boH+jnWoqK2h9D/ApAdKIDSp7DaCzBMmhmFWGmEAuXGDcxrtv0EA4YsrWn2CMIKAgLaLtyqlVOys37C5hZEpRSnYk1adbE/ZaVqIrIexzpxtIQMgabuoskFki7xdsMJdFRPw0Mpd12nnvHp2ELmFaPt+KjWzMCw4irwACPPfQp6PWVjm7CSvW7cOu3btwoMPPtjx2nXXXZfdf85zntN1f1uDO1397aHEOvj3338/fu/3fm/a7T/wgQ9k9x944AEMDAzgxBNPzJ676667ptzff/3JT35y7rXicX7t135t2uNs3bqV2z8xC4MQ+ZTjfghLdKVpTuRXxf206NoaoDlGdVbWEIQlSiMTIr+tTY+zpDGtrgtBjmu2naQUNltTbPsoz0TMRIjeytX2eIuNlG5i5cP1uMwyQGtNjocMSR146o3JmRABq5Cvclp792Hilhuw5tRfg4xKptUTyDZMHqSNEiPMF3iq1oATZQ1M+yffcY3K3R3MA48BEwfoftwEhtbT91DFgCxEnZM2OcxC0DY6JRvWIXxpS5W8zChLcVu/HpnehOvMkD0nO59jGGbWzNnKnHjiidBa40c/+hFGR0dzr11++eXZfdtLuYitad64ceNch7KoHHPMMdi8eTMA4Pvf//6U2/7gBz8AQCnoRx99dO61X//1X8/uT3WcXbt24Z577gEAnHHGGbMZMsMsLL6zGVXyk4owAgbXA8MbKa1teAPVXvVT8BNEwMAIbV+MmIYRPV8bNvXKrPbJMEsFrRSwfyfd9j4M3ar33lgpKstoTgCN0c6UU2bVUH/oYdz07F/HTy58C27f/ieA1pTiXBsmp9SiFS0Urj+CnEdfdLJUNZk3cFlJMui9sNjyxFGTmOyOXST1WzOVqvnUaSkpih1VC/ZMuK4C9rHv4AqZ728fVZwNFRwpZpiFYM5O8kte8hIApK68fft21Otk5K644gp87WtfgxACRx99NE455ZSu+99+++0QQuCJT3ziXIcyaz73uc9l9Sm9br6Y13XXXZc9b51cIQRe8YpXAKAI749+9KOu5/rRj36URYBf8YpXdLSZOv7447Po8hVXXJF9nt3GbDn33HNn9b4ZZkEJQlJZrg6zgWeY1Y5SwOQBiKjknINmDx2ONKEetX65Rhof+jEyS47kZ7fhkfPOQXKQgjL7f/hj7L76u6aN4SAwuMZtXK4B646gNOfqELULtDYoLJETOrgeGFjrWgD2WpgdXJu/Xxt23QG0omhxZdC1GAQo8huW6PmohFz3gdowbRuVKZOqNpJfxLUZU9Vh17mhVDHj7LIgzCw4YhFuzMIy57DKhRdeiA996EM4ePAgvvrVr+Kb3/wmarUaDhw4kPWme+tb39p135/85CfYv38/hBB4xjOeMdehLDp/8id/gk996lNI0xR/9Ed/hB/84Ac51elGo5EpfIdhiD/5kz/pepx3vOMduOiii7B//368613vwsc+9rHc6/fdd18mNPbEJz5xdTjJKnVpszJy7RFsnWoaAxCmVmgOaz9+6q49rgyoplYbg6dM/Wup2n9kMmlTrVDWs9ekX6WJl0KlaRU8CEyLI8DVVBlDbLcBaFxzfb8LzSL2E172aE3fexvF8Ps5M8xyw9MVEGEEnbR7lza06kbTwFz7rDgg04lWFPXUij5Pm8LeapASfhAam2FED9PElWzMtDxDa7JraWzqeM01KU1MPa35O8VN6DQ1HQ1SoFKjWmCVkPZifRTQimouVQIdt6Hv/Ckwsh7i6G10ngO7gbiNxqV/jwHp0o5FFGHwtDMgTMmJqAxCb3myafNXc72RAXSVI+7zGiqG1kGXa/QerBMsJTnMaeJ9rjCRY7MQHHiK1QMj9P6DMH/eqcYwy/EyDDN35jy7Xr9+Pf75n/8ZQRBAa41ms4n9+/dnqoGnn3463va2t3Xd1xfI6iXstZw4/vjj8c53vhMAcNttt+GMM87A5Zdfjttuuw2XX345zjjjDNx2220AgHe+85140pOe1PU4F1xwQZZC/fGPfxyvetWr8J//+Z/48Y9/jI997GN4znOeg7GxMUgp8dGPfhRhuEJTSK0BbjWov61K6ZY0ncNonWdlhKHazd7Hsz1ve6XppTG1UFAJ3U9ark2RbQfRbpjHaWfrnV7vod2k/bRyx1YpTRKzdkYmVSv3Hgrtkuxz9r2n8dTvl1k5aE2OQst815MWp5syyxshnTowQKrs1aGe2wIgobw0Me1yuN4eKiXb2G64dN92M2+jlKJrR3PCW1jwWugJQfvWR90xUiMilS3Wwjjfk3S+TDQxcXYya88HaltkbXLcNH+3mBxEMwZtxiAEIAdGKGoalQCtIaSE2PJEyBNPg4jKpm3dELD2MFR+/yKse9WrcOIxa7HlmMPxrKu/icHjj899LCIsQVQG8w7yPCBKFYhyLZ8BKGRnv3mroxEUauyFpMUEdnRXBBxJXvnMi3f1ile8Aj/84Q/xV3/1V7jxxhsxNjaGrVu34tWvfjXe8573dHXiJicn8dnPfhYAUC6X8YIXvGA+hrLo/NVf/RX27NmDf/7nf8btt9+O888/v2Obiy66CB/84Ad7HiMIAnzta1/DOeecg1tvvRVXXnklrrzyytw25XIZH/vYx3D22WfP+3tYMlin1mda41IUxrDHit2xkjZQGSr0RExdqp+UJlpsD2ki1tpEcbOWQ32+B7t630HxvZiVfelFS+z5mdVNu+H6HmsNWt/k7wWzjInKgE4hkpjSSHtFkQES5NOKnLJKbXWXa1jxMlXooas0EIb0GZn+vlproDEGoVLoMCKLY57PHL3McTb20LeFSZuOKyVFhq3TnMYULS2mvCdxZ1s+e0yDUCmNIS0oP1udilINaE1CbjySxl8qA2P7IQbXAgPDCDduQfTrz8fGrVvxhGc9D8FxeQeZ6YINMPjRboZhpmXeQpCnnnoqrrrqqr63j6IIt99+e3bfT0tezkgp8dnPfhavfOUr8alPfQq33nor9u7diw0bNuCZz3wm3vSmN/Xl2G7YsAE//OEP8elPfxpf/OIXceedd2JychKbN2/GC1/4QvzxH/8xTjrppAV4R4uIyhvRjvQk3aXlj+iRgldoxQCVdjrJ/jmkhIvaonAu82+5j++sPa5KkfUVNillZsZScIanSAfTnvKzfZ4VXlcHqhA1DsLOKAXDLCdsW55+/F1hajgZitjGzbwzCgBpi24WrSG0hm43oMOSc4q1Jiti9/XtmladdjdpZvu5c8XkNPsZTjAq5bE5ppD0dJrAWDY7MHJ+04T+DULYMiIhBBAE0KWq58gLaJVCywDSOHhaa5Re8jKIdZtn9xmuJvxFDwgSsERx/qS9kq4ucxCGWaUIrTlMtdIZGxvDyMgIRkdHMTy8jCYaaeIu7qLPyJmQ3VP2cobCUBl0q+hmxT0nDCNN/8FWnbazdXDlgf6NiEqByVEauwydYnN91NSWhs7ht9v36qWoVGdNVRDl2yExK5Ok7b6/YTnf6ophmNWDtSeA08cACou+xtkVAtrYOGEdV985Li40W/uXbWecX7tNEtNjGZiWRN75AXc8Id0+SlEKuK/2LAPodovaJ2lNdqwykDny2oxBCAG97zHosb0QRz456zOstYZ+/BHIo0+e66e58rGLKpZS1f09hHQBBBm6v1Gp5mrZF4jlNE+1Y/3K2k2oLWCgoq4UfufAnmXxGa0UVmgxK7MiCIxTaVfMs7qrWazryAAoD1J6miVpOwEOIUgxMnOUhUtR8p1WGc5sldXWK6mU5huNMfdaqWpW7Y0DrYyYlxU+sau7ueg2kE1cAG5ptFoIS6SAar8TDMOsUjz7JyWVDiVNIwxpHF8ZUfaJDMnR1Jr8V1vSY21KzkH27aqJODYnkMWAtaZzCWFEucz+yu7n7+/HjjV0uZL1DRbKRJIrNbdInSZUt1wq27NDxy1y8Ec2QKzd1DFesWbTrD69FUkmoNamuUTJazdVFLhTysuQ84ICZlEFADnVC+wkM8xShGfYzNLGpmw1xvMX9F4UDUKakDMsg+4iGrnHwi5hdx7XTiySFhmPfpVVrZiJn1KdrbB7KbTKKH8WEzv8x35qVFCi+jNOuV09cGo9wzAydLZDSOqGoEskqGVTZqtVIGlAQ0NokHNaEJbSxhZl1s5be4WQRhRSmHRrE3EMSrSdUmZ7PyoNZLoaIjQvaQgZQGgFHYTGgad0by0Dd+4gJEceyNLEEYQQZhFYa+0cfDvE0iquSy9ixRwBIzoaeOraIWXNpQnNF3wxtl7wQmxfCPPfQp6PWVjYSWaWB0UHOUsH85zIIHKRYcCoehoF6jR2oiBxy7WO6peOlLRD0H5Eys56sF6kbdN3kWEYhlk1VGpAuwVAO7GzMCJHKGmTXTN2UJg02442SECXumThyn+SNrK61bBC50KJ7A7gBKBsf2Arbilk3jZrd47c9L4jG0vTBnGL7LiUNGZ/kVgDxuOHSFNaJGZmTnGB3y58BJGbf6xmYTyG8ejrKhME7kclhECSJF1fmwvF4zJMHn+ZG92jykHUxZn1SFNXA5WmQBp2OsqVGvVJLqZ0dxPu6oeo7FphAIV64pJJq1bovpyPzn1yYzpEzjrDMAyzNBHSODHaZZf4kUSb7RR7gltpm7QMpipVKtdMpDE2tccg2xNGZCdzmh6ahCuTNhB7abphmbZXCqLdcOez2dd+urR/LIDqjQFT75yPZApVsOVhOLUa+mpDBvTZ23Rrf16TJi5YEDddP2yVUFp+qcJCXXOAP7mVTV9OslUZ7KbxNdVrDDNvlCqd7aB8RNBZnysDLzVN0AQg9VKN0qTTSQ4iYGDEPY5bpv7Kq0nuN9UaIOMzlciS1qbvZJo3VNJrNZWpTsKrOTPvjWEYhlk9xC1nC6MK2Ubr1NpF1nYrv48QWY1yV0e5PODsZ2BsnK1RTdp080WehCSnWXuqyEICcYMcscCUJCWpJzZZXHgu2DSgc6EbMJokKe3u18n2m3W1GhCCvgfosnBQ/Jy0YrFPhumTvmfZUznB7CAz84YV6Eo9oy+kqyvOynL91hMAkFLdsgzcZMAXr4B2YiOWblkQcYuOY1uOSAmqx1I0htmWADfGSSU7NJMApaBtP2Yj2CWgTK9Ls8orJDn/fhsqq3CdJnTMysDMnHaGYRhm+RJ7DnDcoBtMujRA/6q4y46mxVPOCTV1vmN7yd6Va2ZhdzAvBgXQvpVBOk3TRCaFcLa2VXfObupFou15CmitIeIW9W+2NqzDQfZ0OrSCVgpCSlNPLTmK1w9BSG257GfZnKQOIMV5QxLT3EuAytJYFJRh0Fd1vlIqu6WFBvD+a3O5FY/LrFJU6jnIAKCN0+u1gMoWZbrUNdm0ZpUYA184Vu5c+RQu3ZiAro9CQ0NrRcak5fWCDEISM0lnUBagNbV7ak44o+MbeCkhwpIpCQu8FXz70yzWYhfSydtNMAzDMKuEzLkpqkl7KOW6QghBtsV2TCj2VwZowXjyoHs+ifO2z1IfAybH8s9Bd7ZXBFx3CADWiddKUc9jrSCMWKWIW1nPZHsDQNvSnewdijSh95bGU2eWMQ4Z0OKHt9iQzWOaEzTPiZuUkq0Vfb4xzyv6wSZJLORttjz88MP43//7f2Pbtm2oVquoVCo45phjcMEFF+DnP/95z/2uueYanHPOOdiwYQOq1SpOOOEE/MVf/AUmJrr85lcgLGHHLAO6ZSr0cbXoRw3bbhq3gIn95oHnhPuOdNbpov/jot2gyUbnGY0jXKhz9q+EviJ2r2yNmYyFYRiGWd6UqmbBtcv0LSvT0RR5hYZOE+ikDR1GsHbGLgRrv6TH2ps0pawrlRjBS2On4picp7gJtNtOpMsuRict95xxjrV/s6rVxq6qxqQbt7VxSRtQKbTNmIKAliFFkAGXidUtLZvpTUdplokop+ZvzIvtK5pbbrkFJ598Mj7+8Y9jcnISL3rRi3DOOedACIF//dd/xamnnoqvfOUrHftdeuml+M3f/E185zvfwUknnYSXv/zlGB0dxYc+9CGceuqp2Lt37yK8m4WFnWRmaRFYQQ6TPiYDJ9Ah7OM+lRena5njK35aZzhuufqq6qCpwTIGXCWuXqtflOm5DHR1dAXQ6RQXlwx7PRaC2wIxDMOsJtp111rQR5sIq3ZZecKm1wIQxmbYTk1Ca2d/pKS6ZCHQkb3UbtJCr6/noWI6V1pI6/ae0wA5YZMHM+cXQQAhJbV2OrAbgMicfmGFvYzNFUK4m0bWsgrmOdbkmAE2hV4GJBgaWcXybtvKfJcQpidiEW6zYfv27RgfH8f27dvxwAMP4Otf/zr+/d//Hffeey/e8573IEkSbN++Hc2mWyy5/fbb8Wd/9mcIggDf/va38f3vfx9XXHEF7rvvPrzwhS/E3XffjTe/+c2zHNHygWfYzNIjKlPNTHWILuylKjCwBqgOU62Mn+7cKwdFSDIGU/UR9g18yQigaNO2oraGDEplkLaTwrXWmImTXLbbi/x+vlhJ8XG36LA3QfB2YoVPhmGY1YRdyLWpsR2Y6bSQZHNqQyZFOfFfBWQA6j5strWLwTL0SoNgnPGCQ+UvVPfq+hC3KTurXIMIqa2T7+jKLU/y7KNJp7b9nItisGFEjrH/LiVHkmdEWDJzqKqrKbf4yuLVQdY5WUHs27cPv/jFLwAAH/zgBxFFbk4spcTFF1+MarWKgwcP4s4778xeu+SSS6C1xhve8AacffbZ2fO1Wg2f/exnIaXElVdeibvuumvh3swiwE4ys7SwK9+NMaeeCVCNVGMMaI67Oqeu6VY2aquA1sQ0vZDd/kIIiJFNwMgmiKgE0Rilc0YVYGiDN4mYYXqzDIDhDU4ETAbIqVV3vH/V/fWuIhosmMcwDLNq0YoWdENvsVQIagfkO69Jm5xOW9ObbSq87oOyi031bEwQkFCkFffKng+BwXV5IUwhyAFeezg5yB3DNunU9py5BWSZ7+tsxxuE3kKy5kjyTGk3jD7KOHJ/16hKAYnaCP0r2C1YSZTL/fe83rBhAwCg3W7j29/+NgDgNa95Tcd2Rx11FM444wwAwFVXXTUPo1y68K+BWVqoxKWRWUVpIC8kYSOqXet0C8/ZdhTd8I231kC7CeErYLfr5JA3J5zTmsYzaz2hNTn3NmrtC6kUIwGmBovOJfITgl5iYSywwTAMs3rwF36D0CwcF+xDRNvY9sSoDblIrLFBWmtnLbV26scqcfbGd2CDUg99DGE6S0ivFEia81GkWtvUbqWg09QodOf1NwQktAigfSdNKyBtU/20fWwEvzreM9MbrfKq6D6hWWzgGu8ZIyEW/DZTBgcH8dznPhcA8J73vAdx7DIolVK4+OKL0Wg0cPbZZ2Pr1q0AgHvuuQf1Os2FTz311K7Htc/ffvvtMx7TcmJel+JuvPFGfOlLX8KPf/xjPPjggxgbG0OS9HchE0L0vS2zgimuYtrHQnQGTvu6qJuap8SoRttZQxDlo7MqoVYaObEsZRQfCyeeiTFpm76RYWE1L6c4mh9uDv/c1sH26ZpuxzAMw6xIygPOPkG7RduwZGpOo0w7Q8QtIG5B+JlJAoBppZTH2BZrU6y9icpkq5TX1knDlSfFTXouhallFSTiZW2XUbnWxmkW5pg6TSCk5/BrZSLItEAsAJdy3cX0ccRzJhQ+PNvHeqYaK8yy5NOf/jTOOeccfOpTn8K3v/1tnHrqqQiCALfffjseffRRvO51r8PHPvaxbPsHHngAALBmzRoMDQ11PaZ1qO22K5V5cZL379+P17/+9bj66quz57h3MjMrZGCc2jZdwK0jW6q5FgUW62iGEaVGS6OsaaPPQlLKl03rSr2oa1TJW92uAtqB97zZtlybmVGZ1sEWpuWTsEVZ3oD6cMZ5osAwDLN6yFozdbEtvuCSEG5bP5VaBrQorP1IriTblrTISbbp0EqRkxxGQKMNCOv4Kq9dYcFOSQmrDiaUgs7scUDaHnZ4ktLAs1Im67RbG68BoRJToiTyJjGIWI9jJghBqfLtFn3OpSpHjueBuYhpzfZ8ADA2lm/DVi6Xp0yr3rZtG26++Wa87nWvw3e/+108+uij2Wsnnnginve852F4eDh7bnx8HAAwMDDQ85iDg4Ndx7LSmPMMO0kSvOhFL8LVV1/d0eeOYWZFWCLBLN+RldKpMwKU+lUZogt/WKJ/S9W86Fe55o5ZTJEuPg5COqZFmN6CmZNtjIxfF9UP5aprV2FTxLU30bDpbLavZTYeMxYb9QacEIsbJKtQrgS0iQbxdZNhmOnI9R62dyVlK1k7olLqaRyYaKF1MoMQ5JAGyIS6rPJxczyrWdVCUIqzDKhUqFUHgoB6HKcppei2qL+uFgGZNSFdv10B6LgNncQQQkJAGAfZNYXSWtPx2s1c2nV2U6mJeJpop3WiZeQpcTN9E0QkylWsKWeWHVu3bsXIyEh2u+SSS6bc/qabbsIpp5yCO+64A1/84hexa9cu7N+/H9/85jcRxzEuuugiXHTRRQs0+uXFnCPJn/zkJ/HTn/40q3d51atehf/1v/4Xnva0p2H9+vUIQxZXYOYJa8z7QaVGwVF3F8Jq1wHpOd3WCUaXlbPaiKlZbgBpHYDZtp+IspDAwFqaZKjEvYe4DcCLeNuJTGpW5lXi2nLYHoZau1Q4GfBEYSWgNU1MbT/SgTWc/sYwTJ8YxzMrDbLaGakT0vIFsor7Zgu3Tv9Daw3s30k1rGsOg64M0PxOa+id9wFpAnH0Sdm2+tFfQf/HFyBefiFw2JFmWwW941cQW57oxgBhsr7a+VrkqAQonYsyAzC1soXyJ9uiqjmeXzRnmEWgV3OVQ3k+ANixY0cu8jtVFPngwYM499xzsXfvXtx888141rOelb32spe9DCeeeCJOOeUU/PM//zN+//d/H89//vOzFOvJycleh8XExAQA5MaxEpmzB3vFFVdk9//+7/8eb3vb2+Z6SIaZO0kbWTpa0UG2K+2ToxR5jpu0bRBRZLabIrbWXssoTWJevcS0LErRREUYZ1dWXHq033NSezVgQL4uOmnnz2tX/lVq+jZP0eKKWdpoBdStWrtw4ip+BgTDMIxPGBkRpmLmibEjivoMd2hYBCV6PjECTv5rSQsIIuikBTQmadHWRKyd+rUABtcC7QbVEwchhBDQjXHgiGOBMMiCJbpVh971EOTWJ7lzSAlRqmbilwLwyoWMTe7o4lBY5PbLi5I2Z1Ixq5Lh4eG+ndNvf/vbePzxx3HcccflHGTLsccei2c961m47rrrcM011+D5z38+jj76aADkYI+Pj3etS96xYwcAZNuuVOacbv3LX/4SQghs27aNHWRm6TBVrW6Wrmaiw1bVM03M4y5prx2CWX0oXEvpVsCtQnavnpIZOp+SPdX74Hrk5YtNh9Rpfjm6Q0yHYRjGo9dCrlJAHFNUFia9WilqD1UeIKGtqGzKfwoRWBFAByXoiVFKs47KwMgmih4DrowuioBKDZg8AF0fg9q3E3rfLogTngI9cRDpo/dA7X4Q6qH/AfbtQnrdv0M9dDdQG4aoDptevUPksIflfCQ4LHe3abYURc/ANjIOrWlRpVX3Fty919oNykJI2nTfzomYFcHDDz8MYOqI78jICADSlwKohrlWo8X62267res+9vmnP/3p8zbWpcicrzI2HH/66afPeTAMM2+EJappDiIS/QpLdCtVF964alOH1Zx0dciVARpbWKYxWSGSUtUplEYVV58deq/b98SpZssTm8Xg1yELQRPZogo6wzCMQcdt6AO7nBiWfR6gFGftleNYvYtSheyHEFS6YyPNlrAE1W5C3/sTcpBkAFEbAaIyRHUQevIgRZobY+56ZZwr/eiDEBufADQnISZHgV/+BPp/fgJ91y+go4jaN1Wqxm4ZPY/GhDu3DIytLtOCob9ALaSrsfaj4kFoxMS6LBQwnaQJZcvZ2nJ/gT9ukgOdxECrQY5y0qb7zLSIRbjNlCOOOAIAcNddd2F0dLTj9TiO8dOf/hQAcMwxxwAASqUSXvrSlwIAvvjFL3bs89BDD+GHP/whAODcc8+dxaiWD3P2FjZv3kwH4ggIs5SwrSvKNZoglKrkZMqABL+kSYO2TrOtC/ZVH31BpWJ0WfZRqWDrh4V3efMnMeWamcCU6H5kHOaqESSLyrRvEOYjAfY9McsPlQKTB9GRKlkeYEEVhmF6olUKff/t0I/dC/3QLymymyuKNO2V/LaJtKd/kM7jJm3oW6+GttktJhVaANBKAQcfh979MOlopAr6gbuhxw5CPXQf5NbjINYeBmzcCggBPTEObD4K8kknQhx7IvDEk137KaVMF4rA9VTO8IW7jD2WXWycXUwsdqdgetOt3MySayE59feEWZ6cffbZGBgYQKPRwB/8wR9ktcQA0G638fa3vx0PP/wwoijCq171quy1//N//g+EEPiXf/kXfOc738mer9fruOiii5CmKV75ylfihBNOWND3s9DMuSb5tNNOwwMPPIC77rprPsbDMIcGlZoVbKPsWRtxRrbSRaxLa0pBsquuZRP5TWPMSLhLGZERrbknIdO9jr0yyFERhmGmpt107Q2hoScOQqw7nJ7TGlpIiCiE1jqrDUZYymdORRUSrfQdJa2BwRHSRxgYBvbtgh5cS26r1sDEAYo2rt8MWRkAfm0j0u9cAXHEVgACqA5CygC6Oozg+NMgwgi6Pg4ZBOTIy4D2b9fNCb1FaH9B2n9OayBpujZTgKlNNnoglUF2kvsljFzfahnk5yBB2JmCDXB7rT4R5r+FPN9M2bhxIz7xiU/gDW94A77yla/g+uuvxzOf+UxEUYTbbrsNjz76KKSU+OhHP4pjjz022+/pT386PvKRj+BP//RPcc455+Css87Cpk2bcMMNN+Cxxx7Dtm3b8IlPfGI+396SZM7h3+3btwMAbrnlFvzqV7+a84AY5pCQxMhWSlXqDG/cAiYPkMKwn4bkbwOQc1OuufZS1vD7+yZtemxvrUky6BCUHs1q1EwQIZc0VRthB5lhmOkpVZ1QlRAQQ2uBuEWOaGMcwmTzCSGAqArUhjtFAMMIqA7nu0QIAbHpKKAxAT05AbH1hPxUfGAE4qiTIcpOJEv++kugH7wXCCMI43SJUsU4tzEpVhtFbSFNa6iMgkClfa40QLbV9oC2UfKBtWQ/LUXbzEyNkJQ9Vx3qnIMUF20rg/T94Ey1FcXv//7v47bbbsOFF16IoaEhXHvttbj66qsRhiFe+9rX4uabb8ab3/zmjv3e/va347/+67/w4he/GL/4xS/w9a9/HYODg3j3u9+NW2+9FRs2bFiEd7OwCD0PTY23b9+Oz3zmM3jWs56Fa6+9Niv4ZpYGY2NjGBkZwejo6IqXa+9J0qZ6HEt1mIxF/aB7zqpbtxumlaNniEuFFGetC/uW8orVGcKlkFU7FQKZVYhStHrPmQUMw8wAnSZUrhGEEFbrAoCWUWavBEDOjlWobjfpyajqhAG1ptRqu78IgNHHgXINQpLN0iCHW2tNxzQlShoAmg0jOggSAcsi1yGESeHVWpMDHYT5YkoZUqRSK+c820wrK3Rp032DEmVtdbPfXOI3d9pN9zcQws2LFoHlNE+1Y/3W+idgYAG/h5NK4WX7HlsWn9FKYV6aGH/84x9HHMf4/Oc/j2c84xn467/+a7zsZS9DEPAEkFki2GhdmpKza5WnfYQgcS2/btg6MtOtrAq4NLFerzMMQN89yQJdDMPMDCGEyUaxdoZaxwkpAW0WY8PItVJq1b0Wgw1XWiQoTRpxm65HaQIEEoACtBeRFgGENtFGlQJCQ8gQEBp++yYN4YS27Mis7RTCjNcIiVUG3L723CrNp/2GVo8jIifOanj49puZO1b3RCuTms8TlZkgRWd770N9PmZhmbOT/IIXvCC7Xy6Xcffdd+O8885DpVLBtm3bMDIy4upjpkAIgWuvvXauw2FWMzYNy9hjQrh/rNqn1mTMpaQV93aDjHZUAZLx/DFLVTLiaZLv4SgEUB4E4oYTAFNlSrG2dVVRmfbTpgezXSW3ip1COAGTNKFBSumN0bYHktQTWQT0PpTyJh8MwzDMikdrUsW3130T7QUEhK33lQFQqUEnMdkMGwUGnHp0tj851DpuAQcfo5KkdhPa6nUkCWCDwLb/cRpDxK38sCCM3aVMKh1ErvZVGDExOwopAX/FWASAUIAupP0GZlG6Nemes8Kbvv1m5oadp2iVn4MwDANgHpzk66+/PucE2/ScRqOBn//8530dIyc0wTCzIWkbgyrcyrkoKmh6aG1a7kRAJTS9jMeR87BLVVqJbzfcY6/GSwcBRDBENWEQlFpWHc5PRCDpuEkbEPUe49LmtN55lXJiG8UIta3Vqgyxo8wwDLMasK2btHZpz0kbUApaSppDqRS6MQGM7qJ9KkPQpQqlZlsbVzFlP81JQCW03/BG6FYdmDxIbaZu/Bbkc18OEUWmr7KAThLoB34JHHlCNl/LHGSo/DhlSGnhtWFoGdD2WpnINiiarFJgYh9tKwMYPW1ynJtmsdqWo1ghTd92lgfyC9fM7NAKGN9n5k0CGFzHn2ufzLYt01zOxyws8/JL6FXWPA/lzgzTH1b1U4i8w9mLtA1MtqjmKW661C4pAWjTYzmk3pCWuAmUa9QqY98jNDkJoswp1zIgRzkqOce2qCLazXEv/kzMxCe3XxGtKVIQsMAGwzDMisdGZ1VK/ZAbk5TJFJazxVsNQVFha2Oa4/SanzKtUpedBC9IUapC79sJ/dPrAcTQD90JccKp2elFGEIJDaFSU2dsj1G0TwJoGrs5edC1OLSL17FJ+/YVlwGTWRV6Ct4+XcS+kjY7c/NBEnstnzT9XfhzZRgA8+AkX3fddfMxDobpnyQ2jq30UpHNa1krCfQu4NDaGWNfOCRNAGlql63zGUTOaNvnGuPGYR7sUMTWUcnL9O5Wo2wjAYVIMoR73k5ipltkElzzzzAMsyoQgtTw4xZ1VLD2zzibujxADqjV24gq3Vv5NCcKhzVp22kKfedtQNKGOPokiM3HQe95BOLwoyjgkcTA5BjUL38IHPFEBOue0N1GJZ6SdVgGSjZN2kS6tQb27wQggEBSH2UNY4NTZ7ftsVMTQfftqRDsyM0Xxc+RF977hiPJK585X2XOOuus+RgHw/SH1q7fot/wXplJQeYkK+OI2ps1uEYcREhylHNiISUTQfaETyqDzpG2E46cIrF3bDs+/1+LDOhY2Uq+cM9D0DhU6omsKKoVgnDjtHXINoLMtUMMwzCrBt1uUF0yQPagVDOpyaZTQ1Sh7CiVQFiRLpU4Iay4SbXJ0pv6yRB6bD/0jV+nDKs1GyFPeCa9NrwO6qH/odrlA7vJLq3ZCLn+CVkEWmtN9rfdoHOVKjSesASEJQgreikC6CCkvssWUXLp2db2KRNdDiKvt7J5vwJkF2XITvJ8IQNKsY5b2d+MYRiCrzLMMqOXerRxJosq1FaQwgp+pLFzUIOQ0sDaRj2zNuQpdnrHLVXzz1UGgeGNVCNl21toADKAEIFLqdba+OjSpWBLs/ovAxMBl3SMMKJJhp9q5k8EfKV4nhwwDMOsKnSaAI/dCwyscdE+YcQnGxNk25KY+iMXbZY0wpRxE7msJQC6PgF9638B7RbZS28RWGsN7NkJfXAXsPV4YGQjxKYjyQmHscYyoPrm1qQRpVRkV6NKZ+iraF81kGshldu2x3NSOsFLZvbY7DkraBqWuCUhwxTg2TazvBC+sffwo78WrSk1uih6RXeA6oDr2WjFsPoZgjDiFoPrnNgFYKLX0t2HUa1u18kRRxO51h32frlmVvoLE4jWJE2A2HAxDMOsblJTO9oYJ0dZSIhyFYAGKgPQY/toobVUhRhYS2nPSdssHpuWc+XBvEClkBCVCoLnvxL6kfugbrsGaExCx22KAKcJ9L7HgMlRiMOPplZT7ToAAR1GmW0SAyPQ1SHg4G4gVUBtxIiFwS1SQ0PECXRliFK+pQTK1c6yJD9jytp6u7AMGFsKspucGjx7WpNG0bpAdYjnHH0izH8LeT5mYWEnmVl+RGWXnmzxVKczVNq7rjeNTTsN6aK3g2tnlmrUqjvxEQhKY/NXxf36qQ68cSUxTWL89+OPkw0WwzDM6iaq0KJpc4LUiIc3wNoRrTVFj2vDEEFAwlmVQVKA9gkjWty1NCecKTriWOCutZCnPAs4+Bh0VAbKAxDrDwee8XwInVJ9sO3TbOyrMIvTQkraZ3gjhBWe9G2hSacWAsDGI8nu2s4RtsZYhpmgWPb8yCZyjJN86ykkMTvJs8VGkbvBcw6GyWAnmVmeBKFzKmWXr7FWbsW54zX7r6YaLEuzDgz26SRrnTfa2jq4Xgsp2/aCHvQ+ViYSVqiRBrq/N4ZhGGZVIYSAPvw4iuQmsRG8orRpIQR0ZQC5xdd2EygJ54iWa53Oj29HlYJ8xnOpNhWgf8s1iJOfTY5rGpNjpVKKNgpBHR5sbTIArD0cwne+gtCdU0hKxxaS6pJ9TQ2taexpGxTh9jK+Jg/Sa8VMKy47mh1au+9EN3jO0TczSECct/MxC8u8/xruvvtuXHPNNfjZz36GvXv3Ynx8HMpvZ9MDIQSuvfba+R4Os1IJy0bdWXdfTY7bnuMKWlG3ytFxq3N7IF/32xcF0S7A1SEXI9hC0hi0iW4HkamPlq6OOizlWnOQOBev6DIMwzAg56Zt0o/twmuaADKEEEWxSJMabW1Ru0HRZR9jR3VrknoW50qTJIloCePYhmWKJJcoRVoA0LY3s/ZSvy2+g0wHpCwwa39TRccMI3rOX2jWoOd9jQ5bRx2W6djsJM+ONCkIlpq/gUqNYBp/rgxjmbdfw/333483v/nNs3J0sz59DGPRmgykFZVIE9O6yao9J24F3LaICEJyLP3vkq8ibQU/bDsJ2xs5bpLDXRmgc7TqTsBE0b/aCoAFAU1NrGBK0nJCXUlsDIypRYapS7aiGLb/YFR2K+dJ26hKhq6uWgh6XisgNf2QbYsMoQFImh9ZBVBm5WFF5rQ2iyfme6xS8z0sTkCXOL5ono1U2N938TfLMEwHOk2AfY8Y4S5JOUu2g0PcMqrStsQnMDaD2itpY5/0fT+HGNkAsXYTdNwimzW+32VU2dZRUpJdjZtOswMABteYvrqaziSMinUQIrOrYUh206Zl595EZ8BE+6nY/gvdyo/ogwA0d3aYMVrRNVd1KUGzPbit+JudhzDMKmdenOSf/exneN7znofx8XG64E2DdYj72ZZZpbQm8+2aAOdY+qZUKWSpWO0GpZSVqiZSaxzVsOx6RzYn3Gp5VDVtn8rm8BoY3QOkMQkkWNVpJWnVXCkIZcZka7OGvPqubj0pAeg0BSb3U3ocAFSHnRNMbxCITYQ7qlE9mX2f3cTIfONVqnLLhpVIu+HS+dPYtQ+zPU5jLJ6oW5bmOQOF2dakm/RGFZO6aX5LYbm7pgDDMA5lFoMb46RgbTs2QFMWVNJ2i2dpbLonCNokTYD6GBCVoH7xA8inP9+1b9Km5ZJtTViuQaw5HHr3/WSTbHSxNgIBWrTTiTm+jCBCY6PsWGRAbag6HGLTa1mpbHFM6xQiSaFBNbI6CMn2ak3nFsK7zmjaN00ANUl2tNTd5jIFtKa/vz/nsNfuxHzOOnV1ymGpUyGd6UCa20Kej1lY5uwkx3GM8847D2NjYwCAs88+GxdeeCG+9KUv4Wtf+xqEEPje976H8fFxPPTQQ/jBD36Ar3/962i32xgcHMRHPvIRHH/88XN+I8wKo1ibmyE8NUwTWc7UpU00F8I5GL4TaSf32TmS7DWtFLB/J0WKK4OU3eArV4OESaAFtBAQQWTONTU6iYGDuyAicx4AwneQNcx7MMR15xgXF5G6LSolbXaSVyL+99/eL0ZWbHrcQpLGzlEPos70zW5onR+77yDbxwzDTE1YAipDpE5dHwXWbyGHGXDCWNJEdLX2an5NDWp1EHJoHfTaTZkzJGQAPbyeHObmBNmTtU8AWnWINYdBqxQ48BgwvMUt8gKulrk2lBffUooyslpef2MhkBOJsplSUYXEwAAIIaGtTbX20GaYKEWRcSsoJU12VtJiJ7lfvGi9eQK5YEOa5MvTkpidZIbBPDjJX/jCF/Dggw9CCIHXve51+NznPgcA+MEPfpBtc9ZZZ2X33/rWt2Lnzp3Yvn07/uM//gPvfOc78Z3vfAenn376XIfCLGfiFhlbIWjiHZTyolpFim0jIMh4BmFeUCs26dCtOjpSv3yBinbDROw8NVBhUrN9Y+JHzvqp3WlNUmqaUuRkA/Te2pOm1QU6o8MzgZMxlidJm76TQphadeWcz8oQTYizNmdWvEbk8xEXQ2Al9n6TtiVNUVCniE2xVl6UAsL9vnmRh2GmRQgBrNsM3W4CrUkI297QlA5pBUqdrtQgbJTZ2pU0haia35mwZUcC2l9kKw8AYRmiPuoW32QArN9CEeScLRRAZQCiuFAclpzTrDWgUzJRKoWw1yuVQksJNMcgompewCsIoFVKzrs9m5TQ7QYQhPmyvOVUbrLY2LK13EKr9/cMI8pmY2aEF6JZsPMxC8uco/ff+ta3AABhGOLDH/5wX/ts3rwZ3/jGN/Bbv/VbGBsbw/nnn4/R0dG5DoVZrmhtei+a1U6bNl0eJIe5MuQiwjln0jPYtsbYpo5lmwjjqJoosu9Y+wbDimep1CsDMBEw4xhoU4eVjaAfI21rhpMYOo6B2hpa/U5NO43imHyEETpJTRqUSjrfR7gIjhIzd1qTyH3fm5Pu79qapJTksIy8WdTkbJZq9JuQi5B8Vfxt9Wu2yzUad3mAfsf+77tHmQLDMJ2Idj0f1U1TqldOTS1pmkBrBVWfgBrdR0JfUQStnRI12k1oGXSUvonC4jP9wrV7lCZAmkBYbQ6taNG3PGBSrG1NNHV2sPZSyAA6aUMnbWOuTRp12nYp32ZbaO3GaV8LQrOerJ3zz3oc/SMEpaf78ylIlw0UFWzNdAufDLNKmPMv4fbbb4cQAqeddho2bNjQ/4mlxCc/+UmUSiXs2LEDn//85+c6FGaloZWrX7ITa39C7U/SrfHUKW0XlmmfqXole/0YRVgC1h7uHVo4JU3jQAspSTfLThr6qaMs14Ch9dQyY3CNWwkfWm/EMUy9V3GIQpp66QrVn4UlmoTIwNRoGQMXcBRueVLITigu/ti+oR2Y73gXAZwFISrTBCsq02Sr3wwIIeh3ZN9T9phFuxhmRqjC4mqpAoQlCBmQjQJIlGvvDohKjX6rUYVSaJM2dBJD2DRl3yH2F42Vct0W/HNFFbr5NtWKC/oLuFpnmS7aOL1UHhK6x/7pzevajiFumrrnNGsbpYU0mSvGQWYneWZoU9OtEqMwrimbJzX9qrvFH5ipMe3XFurGtnLhmbOTvHfvXgDAE5/4xNzzgddOp9Ho3pPtsMMOw5lnngmtNb761a/OdSjMcsWmWFu15lKNomlxk25+Tz9/hbOjzZIwGUQymzhkz/czjKgMEUS0Sq+MenYa550SYY6vFEX/pn1rAiKq0L9pTO8raRvFa6P+qVJ33EykTDnla+sYuwbPdCvWdjJLH/udKg84QZxSzZQYmLYmVVPnG0RuopqlLBuBN18IayERgibJpS49VxmGOfQozxFNEgCaSnn8FoZBBJSr+YU2acqO2g3oNMkm3QLIaoOzBTsZdO+rXKrQcYsdJFKjBeJTHYIIy66uuFyjMVoRTa1ypRZCKwjryAFmkTzNDiukZx+tMBnTH1pTLXvc7GyBGbfo+eLCB8Mwc69JbrcpFbVWy0fVhoaGsvt79uzBUUcd1XX/o48+GgC1kGJWMUEEVL2V4dyqtm31lNCFvpjmaQ221oVex12c45xx957Xiuo+rSCYCPIp3JmTbB4L9Gek08TVmtoTJm26WTETe8xSmZwffyEgM2q9ouFcR7RssM4tYL7vw96LEqiN5CcnQuTFU5J2vm5sMYS7GIZZXGrDVHsMDUReJpEMqTQnLNGi7KajKKJsrhFCA7pUofIh20LR2hqbsWWrLCtmPleqOO2BIPJEugIn9JTGRl/A7q9dG6nqEASGgMkD2TC1DIBmHYjKTqdDCECQcJcIArpvx2XrmYutQlm0sn86yrrM3yn7F3l9CRZEYxgA8+Akr127Fo8//jgmJ/NRtY0bN2b377nnnp5O8p49ewC4iDTDAMgLd1lDaKOmfisovy7TpmVbpk1J9Q1u7NVTeYbbP7YvhKK1qRmdhq6RXk+0yI4hiPLvLxf9nmJVN+SUs2VDUhS9KvydlQIaYy7CUqpS5Me2Zinmwy2GcBfDMIuLlEBkrvs2zRkgG2IFu2zf4agM3ZyklGshqDbYOkAqBZB6AlkBaWbk0jqNjdUib099JWQ/w6moHaI1OchJnDn0AgJ6YI17PSeGGbhIsn2vwnfofDj1tG+K4omlqtGB8ZAhZThxWm/fsHDXymfO6dZPetKToLXGI488knv+lFNOye5/97vf7bpvs9nErbfeCgAYHh7uug2zSilV6IKdiUqg0ykoXshLtbyTXHSgp7rw+2lpGl4EWeejycqkXkeV/mqSuzkyYeRFi7VJjat529pFgGkoDXBd1nLC/451E0aJm24ialPtMwcZcN+VAUrJXgzhLoZhFpcsMwmmI8MQEFacsCN0JnKlVQqUKhBBvhYYQQgdRNCCaphJ8TrwnFJD0uoj9dbb3raIs4uAzQlPkLCRdZsQJs07V8dsFwJ98bBuC9UWXiDuHyGoc0dlkPRRopJnPwQFJazjvFilPAyzBJnzLOvpT386AOCXv/xl7vnTTz89S7n+9Kc/jbvvvrtj3/e+97147LHHIITA0572tLkOhVlJWOEiP500NEqMVhnTGlAhKaqrU4q8pTEpeppejD3X34R3bBm4OlF/smAdGxmQAy5DJ4TSz2prEJqV9YBWyUOjAFqquhpkq3AMI0giJBDVnEhKVEEm1FUyQiyVQVa2Xm6EZRPtCWnCUvz+FB1nO4nxU+Xsd3K5q4/a73y72ccknGGYDC/SqrWGuvlqqF0P5jeZHIN+9L7seqFNnTHVLocQQUgiXyE5yjowUUR73FYdenQ3qVHb5wBopejmn0wG9KLVTNCK9p8cJdVtIclmaa+MKUO4GtmsnZXN5JL5a4PtBmGFuziTZmYU51SVQQpGlKo0t7LOsUrzfa6ZnohFuDELy5yvMi94wQtw2WWXYc+ePfif//kfnHjiiQCAarWK173udfjHf/xHjI+P47TTTsMFF1yAU045BfV6Hd/4xjdw/fXXZ8e54IIL5joUZjVghYxadRd102m+PjhpoZ/LibZ1wVq5m5DU8iI1h2jH9G8QuRriRBnbTbVXwo8u2wmAUjQOpVwadWr+Z9pjIDFiJyHocRqbc2j6d3Ad7Vc/aI6ZkIHjljnLk7jpvgvNSapBtt+dIDITSSMaF1XoOQ36ngjQBGc5/O3tRGsqZ77pRStUmu9PzjBMb6R0rQrbTehf/hBImsD6w7JN9MN3A3f/BGLThUC5SpFlE6kVQroWS4CzR80JYGANdGsSOPCY+V0OQlcGXbVx3ZSDlI3YoG3faDOt7KKy1jQmK9wFGAetcD2wophWX0EIlz2TS+/2SqGUAnRsFrP7KHliuiMk2ROVUpkPCt8HhmHm7iS/+MUvxsDAACYnJ/GlL30JH/jAB7LXPvCBD+Cb3/wmduzYgYmJCXz84x/veowXvehFeM1rXjPXoTCrBWts09lfyLXWQNwwxgEkdmKFlHJpXWb126ud0mkCTOynl23kGaAJwfAG2mdiPzKV0K54BqkoJmYd8G6GitOgli/+304roN0CYrNiLwMS8iqm8JcqdNMKWSriUsaK6wHIenN2c5RznwV/pxmmbypD0JMHgKQN9cAdwJHHQcTt3CYiDKErNahf3AT5zN8A4JxirRUASWbGRKWz1OfGBET9IBBVKFW7VQfKNUrgbnvlIO0mCYjFTYpCKkX9moPQOM4NWtyToevHHJWMEJdymVqJXXiOgXK36ahNwUZn/TI7cvODXwPuL5wwDDN3J7lSqeDaa6/FgQMHsHbt2txra9euxQ033IDf+73fw80339yxrxACr3/96/FP//RPcx0Gs9oIyy6yC8DzLk0ata1zknlj6gtwJZ6oVhxDlxKIMEIuCh2EdJ7AS33196O4M+2R2N6DJjpd7GHsjyUTNlH5eqysZqzknAtfcIPVPJcvUdlFkkUAtI3YoY2eaJUvAbC0Gk7orTzQo3/yEsEXqrO9UYMuTnJUoQk2QPWUDMP0hf7VL6Bu/hZwxJEunfqo46HrExC1QejmJPSBXcCmIxA85UzTb1hReY5dtE08RWpfybg9CR2EEColkS9oYP9jdG2SgYsMVwZMb93CYq+NNNvnoKm9lAjMwpix03Ezr/0hBNlNu7BsF9GEAGCuiwJZthcA1uOYL2zGj12IBQDJn20/ZP2LF/B8zMIyL7Ot0047redrRx55JG666SbcfPPNuPbaa7Fz505IKXHsscfipS99KbZt2zYfQ2BWG2EEBMNUp2RVGm1dVGUgX8PZGDfG1U8n0uRwxralhQYmDkCXqsC6I4zPKpzx8GujShVgv63Z0c6lDiLaLm6Z+8qpjdqaL39c5UIETRrnXimXegaYtDaVr9dilh9hidRjoandGOB9J2X3v61WnhI6qM5+KTvJQeScX0yRSVGqGOEdwQJkDNMn6kffhf7elcDgEMSTng4xsgF676PQtWHoh+6CgoI86gTIE06DnjgIANR7uDoEhCXoiX3O9skK3Y9q0GOPA6WSE9QSElqn0GMHgUqVJucqdfZr9HHogRGIMrWo00K4a1ponK4g8sqPUtfyzraDEqAFskqNItNZdwdFUWqlKCvF1h4LAUAakcse10tm5tiMH5tVB3APaoYxLNhs69nPfjae/exnL9TpmNWAkFSblD0WJI5VTGGWElDOydV226gMlDaZGidtWmAMQwhJjq5O87XIpuZYpDH08CZ6PaqammgFlKuUQuu37JEhTQaUJsNjV79VSg6PFTWxjnvSpmMFkTNUUZn74a4EtHYK1r56ugyB2lCPFLesGtA8XOITwyAktd2kTd/xdtOI1HV5b/ydZpi+0Qf3Qv/nlwApIbYeD7FxCzm1RzwRqjkJTIxCPuXZ5ACXaxADI64XskppQTiiDCwhBNUlSwkd10GLsyn1MBYCIoiAiVGyaYDZNqDrVNyi28QBaGns18E9bqBpCkQkcCn8LK64ZRaOQ6cBIqXRXPCvD4LsqC11yrV/MtfQIMzXOzMzR6VGbVw4cdAsC2CJ25klghR0W8jzMQvLEg5JMMw0pIkXtYIRP+qSulk2K9UmpTW7zsjA1VMJIFPMTlruuH5vWysuksa0Og+YHpJAliqdJvlosQzcsZqJqdEUJFyEQnQ6aTmlTz9tVSkWNloJtOvu7wvhWoGVq70nJTa10bROydqhLWVkYNTaPfppl8YwzPQo0+ap4CDqRx+COKVLIMLaFiN6KaIy1Rv7VIdoUbY+RoKCQgJDayGH15FythDAuiMA2ws5CIHx/eTw5sqPAAQRRHUwX0YkZN6WhiV6XiunLWJLiVQKKCNq2S3LxAoDQiyP6+FSxG/PZR+XB0zJGPhzZRgDO8nM8qUo3OGnd/kIk6Jl07fsvhpAnDplTsCsUveo+9UK0MLVWgJAfdS93vI3tqnahfY2Wd2pzj8H5AU0er1PO1a/BRaztLHfl9SfmBbagE2Fn22wHP7m3b7zDMPMCbFmA8RLXgv9w6uhB9dQrbFZtEWaAkc/GbrdMLoaoK4LgLm+0LXDXj1EWPIW7EyuimkxKIyoFmllUWq2WHe4Ud8AIAS0DIGRjRDlAaA1AV0ZJJGvsAwMDGXbZXawm20LwvxicGjaOvlKy9m2pXzZCcDXlblSbK9VEmSTGIbJYCeZWb4EESBazvHsq1bTT121Twnns6oUqJRdKyYROPXdqOLSw6ZF0y0IybjbumRbaxyW3Mq6XUEvCjtl5zWrusq06YARJLM9o5mli0ppISUTkdMuw8Au2JQHphZkazfc92KpC3cB9BsJy64V23JoW8UwywD5rN8Afu050PUxCK2h2w1ApZR2ffxJdL1pTrh2hOUa3ZKWy4SyGU9GiVqY8iAhA6BSdWUggtpFifWH08k9n0pEJbK7SZP6L0eU4t2186LWJuNKuPRvgK5pmXCXpGwvwBPuEm7/pN15bBaxnD02xdpmuWmjZM4ZazNCSAGxgDnQousPjDmUzPtsa/fu3bjtttvw4IMPYmxsDHHcj0NBvO9975vv4TArGSHIUbQ1S305jDp/17aWsARRVpucOaN++jRAqWl+BNkNyB1fmFSxIKQUa+sc2WOUqsb59Z6zbYB6nTeN3fFtP11W+Fy6pDGlL1q0ou9rGOW/P3Gz94TP9s+2LHXhLkt1EFC15RP9ZphlgD7wGNX/BiH0wBqKvKYpkHpOsAyA1iT08AaIgTW049AGs2A35kqLAGdD41b+OuPraABucdc60K26sW8RLYhJaUosir917/dvHTNfFEoYx7xVd6ZZa7KDvignNEWTSxXnVPN1ZW6UzN/Caq6kcV49nGGY+XOSf/zjH+P/+//+P1x//fWUojML2EleeWitoB/fAbTqEGsPBwbWAPt3kkGtDUOUqqbFUUyOwkyjTjala0bbGiMbhNlqevZcWPKMr2fcfYKQ3kdqnFSd0oQiMK2alCaFBT/9u1ca+FTvp7hPUejI319roDVJYyrXeJV9KdCc8CYdZkXGttvw25xMKWBVEO5aTkrQy2mszPxjr0lKuTrWuOEycez1vtu10fbaVYom8/0uDKUJOXxS9j72MkArBYzuyRZC9e4dSH52C6LTzqAN0pgcy4E1dD821xIbqV1zGERtxB3QtIpCGJn+xcI/WcFBFuSQxp6ugErN/iWTzQTThUF7DjK8OmSQcnXSyi/2BpG57tlWh6GzxVmdtEnRDgXgZ1jbbRfjT5q0KdItAxMxX57fqw5k6JzkmcylGAALvwa8Ur52y4l5cZI/85nP4A//8A+hlJq1g8z9v1Yo+3eRUwxATx6EOOwYerxxK2DSxYRNP00T+vdQRUetCJJVlc45xDPErtjTAyC7ewid0yAESjUXQfacK92apEkTQMJiQxv4irro2HRB4yhXh93frDzopSNPIZLS7TvLMMuBlidU14jJufDqYBE3XXSxSLvhylGaE0ZMaprrmXXKAcCWqy7XGsvxvUB9FGJwLbRKIUbWInjSCeQ8m5m5iMoQQ+spupwhgLVPgOiVNlsZpM/WXpPCUmdtb1hyIk7aq2uuDufFtwD624XlvFBfVgokyYG2/Y+DkOxWEHRez0KTVaWVp1ptotxJTPssVtaUUtRGEjAp4jJT/V72ZJ011NzmQwyzQpmzk/zLX/4Sf/iHf4jUiNIIIfDMZz4TT3va07B+/XpEEaeDrmZ0Wki3T9pZrROQS342O8xukaVvZLB8J06A6S1b+E1pDUyOAWGYLTbpNMkEXJhFojpkJleaJnv+JE/K/r+Hy/07y6wulCkFKTpf3YSW0hQIdefkfD7sQC8hxCWO1jrrxqDbDaAxCegUYu1G6H27ocYmIY8+DmL9FrqOrNlkym9SYHhjbwcZcHXK+RPS9SVpk6NkW7bVhoGGiRpXh5ymRhKTzkap5mpYK0OUGi0kOdg2iyQqdy4Cii7XM9FjsdBmey0mxZ7BK0kwrNfnzvQFR5JXPnO++lx66aVIUxKOOOmkk/ClL30JJ5100nyMjVkBiLWHQ4/vowjCmsOA4Y3A2D5opYz4R2EyxL1TZ05rEqJM0RitUujHHoA46mTjUDOLhgwoHZJhVhJFrQQflQIT+/MCddYJMz16c05GGlNEueg0lSrUMk/r/tOmc2JEgo6xHNm/E5g8SJH4+hgwvAGiNkRlwqUSRPg45NEnkgo1QD2NN2yd/fl8gS+fIAIG13ZuOzCCDsIICLs8v9xJYujx/Ubx25TNcM0uw6wa5uwkX3fddQCAarWKq6++GkccccScB8WsHERUBo59Gqivo6SV6XVPACJKs+pIs7cp10x/WOVPmAjE5Bh0YwJipaSDMQyzdFApMHGA/i1VKcLoX8OTdr73alQlZzUTPRymfxtjbru43ekkZyKGmFn4JCq7FN5lGHbRWpOoXxKTikFtOKc4LGojCLYMAO0WUOHe44ca3ao7oTAZQGsFkbRWTro1wzBTMmcn+bHHHoMQAi94wQvYQWa6InxBCBvd7BWNWOzUqmWCbtah/vsHQKsB+ZRfh5ABhBDQAyOQT3ke1/gzDDP/tOpOYKndoOhjEFKmULvReT0PI3KcrSq7jQpLr0du0GNRdLbXsGV87RNCkIC0lBCIjJqzgtb0noQMgKgMHbKdXBDaDVrcN/MSgT762jOrBiHEgs61eF638Mz5SjswMIBWq8UOMtMfUZlSuOI2KWzaVX+VdohRMb1Rd/4I2P0wAA11y3cgz3oV9bRk8Y2lgUrNd3yOAnEMMx1aG0EsbYR4ppjEK2Xa6ggXDWtMuF62dhsg319XA10UJJzoj1U8BkxNqyfKZHuxJm26voemp26xT/yhJk2Msx6QenMvVe2k5YlNlaYvWzGLADqJjSiWBkY20rU4d2gNNMehxw9Aj+2DuP8u6FIJGF4H/ciDEPseR6uZItj7EIJtpwDrNkKefCrQblIXiDCChnWW2UleEMIIemwvEG2ix0J0/F0Zhlm5zPlKe8wxx2D//v3Yt2/ffIyHWQ10E/NgZoYviBY3Xa9KZvFJE6eGaifi/H1neqG1KTORs1skbNXd9SBNKAW6F/VRb9uYHOLUtN9LEjcelZI4k+2Na0kTcoK1JkdXBp0CWVaR3W7fjfkWDEpN/XIQ9qiVVkDT/CYTAGXR3TmPm86pB4zQ5JBrFZjGcK3chGnFNOm6eqiUhK123Qd9xAku8qM1MHkQemIf8Oh9wK5HoUUAPOEIIIkh1g5DP/oAon17II49HvrBe4D//jHiR3agdPYrjcp0RMerDPK1fqEYXEd/4+YEUB2i8jG+ljMGFu5a+czZSX7lK1+J2267DTfccAOUUpB88WaYQ47cdhrU+AFKBzvxOZmIC7PIpDGJ7WRIr/8nwxTQ2vXTBkz68gwF9/zvl0qdroM/o9LaKCB7TqtWlM0jok7FXt+58/8FyEGsDLrHUtK4W41O1Xbb0i+NKcX6ULTxiZukBg3Q8bupOxffX6/fZDenPo3pPbfqpm0byFEqD2TbZ10FpCS9jXIl3zO4PgYRNyHKA9Cbj4M48skQ1cFsLLrVAI77NaA6CBGVoFtNtP/tMpRe8mqIdYdRDTdACyAcyVwwhBDAmsMXexgMwywSc55Zb9++HZs3b8aePXtw2WWXzceYGIaZBjG0FsHzz0fw4jdAbt222MNhLEnc+RxPapleaFVQe+4ReZ0KP7KVJsD4PmB0j/suJjEw+jgwtheZNgSQd6Jt2nQ2Lu0cYxlQyrEw9Zhhl0haqQoMrSM1dz8abpWTayPkvB6KUIj/myu2HLTk+tr3iCK36p3tfuDpafg9guM2EDeh62MuigxBbZEgIErVXP2g9vsIl6pAyfsMhaQI5dBa6vgAAVGpIXj9OyEOP5L+vsMb6cbXEoZhmAVjzk7y2rVrcfnll2NgYADvfOc78dnPfnY+xsUwDLP8yEXKTK9RFqNjeiFk3nHst9ZUK8pYmDhgIsJGPMuPkLbr9G9zkiLGMjDbBvnvpBBAWAGgTX/j1PXd9b/PVmDLdxaXAv576fX5CUG9fKvD5LAX09ptLfKUx8/XZOvJUcoCaDeQW3yIytDF4/t/l2KKutbQQegdg84TDrJ6NcMsZaxw10LemIWlL4v8gx/8YNptLrnkErzjHe/A9u3bcdlll+F3fud3cMopp2BkZKTvP+yZZ57Z13YMwzBLkjAixzhNKerDafDMVNga0zQhh7nfBZV200VNW/XetcyNcQAKmQNmRb2KNlmCnDXfeRMSophuLWVnDfJiE1XMAgDyIltJ7FKlw5LrySxDIy7mRXptSyoh6JPyxc9shF0EpjbY3G83gCAgIScvHZ0UqPPHRxpDixIgAwgIQAqosX10f8j2ItYAhPk/+l8wYRiGYQ4JfV2Fn/e8/lvKaK3x3//93/jv//7vGQ1ECIEkmUWqGcMwzFIiiA5N7SWzMvGVoGdL0UmOyvnUbd9+RxX6N81HhHNxUpUCrQnoofUQWrnFniBcegs/okv6tEpdJD2NyYm1db1o0Wdeorph3ZygqLxOgagCHZWRm+1kvZ+9T0ho45gbp1kl5DjTA6B+EBja4D53lZIDboXQlIZOE2ovZN8DjAI2BDntYQmCs1AYZsnCwl0rn76vwFrr6TeaxbYMwzAMw8yAUpWcYJVQ1LJUIQGouEWObFCCUF4drJAANNXMGucQky69WENSW6KBNeTQ2cgqYNKuPSd8qTnJ3ShGu4tCXcqoYTcnIZI2UK5RC6fEOK62htmmnWeOMmimqlJgZBMwuhs6iSHCCICmKDKkqTX3hLsG10FkDjW9FoxsdMeDiSMbwTVdG6ZIOAspMwzDLBp9Oclnnnkm58IzDMMwzFJApUbRWbs0Y62oxVO7AUBCrz+CkneFBFqTtJ/W+SizjWzGTYioAhGVaZG7XQdkRBFVrcmxk9K1WVrqBKGp/U3p/UdlcnRtqy17P1crLCFkiZxVldDnmes5LbwFAk2f1ZonALsfgK4NQ0QhtNY0VyrUmmcOMkBOsFLGGZc0jsCki5t9hBDQLNLFMEsaKQTkAvpGC3kuhujL2l1//fWHeBgMwzAMw/RFq44s/TeNodt1YGwfOVvrt1CktDUBrNsCTB5w+7Ubxvkr5AlKCTHw/7P33nGSlVX+//u591aujtMzPYmZAYYcBMkSDYggoogiuJJcTIvruuuuX1iXBXVXBXUNmJWfgigSVMxKEBEJEgWEIQ0zMDl27kr33uf3x7mpOsx0nu6Z5/161UyFG5+qrrqf55zzOU3x40wBSj1oJy2RVh2Ica2hWoHcNC8nUErOIazwVUoMu/o7E22t6s2zwtFQ0b8DncAVccq1EsfqzavFgyAViOtwTK36fs3a96M2fToQ7jqsf7ZseT/TWZJeqiYwYTAYDDuWGTAlbDAYDAaDIWJg+nClJLW3jXMlCqlScSTV9+tMp3SpJ46oEogxDVonDL5AamLdqojJKHo8wI17OqOGELqWM3SbqFRG3MFD4eyk68c46hstm9RoWPM8ZAuoqJ2TilKmdaWE/+id0LGJytPPkb3gQ5AN3KrdatxLWmsRzU462LCOxbYx7jIYDIYdivkWNhgMBoNhPHge1EoibFKZyReSTqq+FVMoskh4gqSy0NcZtH2yxORae5KSDXKstoW205BKS/q2HaRto2D2Iij11jk3o9TMNqXLFoI0deQ8vJqMjZORfs6VkjxO5yQCP9BfRYH2NWx+RbYVpqAriTKrQCj7Lz4OfV1o38XZey90qYxyUmjfBbcmrtbZlGQAuBXZn2Wjg22Qzs2MtHaDYRfGGHft/JhvYYPBYDAYxorW0LslFlT5xjhSOFlkCoGhlCviLp2VSGipF3JFaUuUzqHLfVGSsErciWqPAZSNyuTRSomAdtJiNGVZkqLse0HdbErEuTODRbKyZLxCkueibMgV48e5Bij3B+7ZIqi158H6l2DOImnnpBS6WoFMLojgW9C5HrVgD5i3GNa+RGqfI6Jl/a0bsXIFKDSLyO6sSKTf94PosR0I5Bk8xgaDwbCTMCEi+dlnn6VarZJKpdhvv/1GvN6yZcuo1Wpks1n23nvviTgUg8FgMBimDt+rjzi6tUkXyRoNucaoblUBtMwdvFz3VmhoBs/DL/di5YrgpGODKUBnpKevyuTrBSRIpLSxbVLPZdpipyBZp01OapBtJ2rNpLWGdEZcwUEmGdI5LGWhtUY3za5bVjW0opoT4znEe2YwGGYGCjWl3gEKE0qeasbdy+Hll1/mwAMP5NBDD+Xqq68e1bpXX301hx56KAcddBDr1q0b76EYDAaDwTC1WHZ9amw6O6m701vWoB/5PfrR36M7Nwy/nNbol/+O//f78Jc9gN60GloXoAakg5sLr1FQaALPRYcp2xCnbwNoot7HUuvtSWup4EWlBrSmMhgMBsO0ZdyR5Jtvvhk/SBW65JJLRrXuhz70Ia677jpc1+Wmm27iox/96HgPx2AwGAyGqUMpKLYG9a1Bf93JInCnVnMWB/16fejdGqTsemjfR3duRFfLqD0OCWqRA2EWulpDvSweqJGDllBRzbP2g5ZRSuqts8UZWRynSz1Q6pFzcDLS5imVlcflXjnfVCZoeeXJLZWR1PagN7KqVdAt7VCroFHilp3Kolc9g795Lf6KF3BOOF3SurVG3/sH9Ovfis7kUJ4rdd7VkrTk8oPa72xRPjvaD3ooe3JcM3ScDQaDYWdh3JHku+++G4B58+Zx+OGHj2rdI488knnz5gFw1113jfdQDAaDwWCYepSSWt6JFshuVQRcrSJCtdKPyuRRrXOheY7UyoYiWCmUZWG1zoW+Lli1TGpfUxmJbjcHqb1R/10VO2An08U9V0Sy9mO3Z8uW+9WSvDaT0Bpd6hVBa1kiSEPX7kof9HfLOfmejHPYI9myJEpc6ZMWTaWeqJ6bVAbQUGgBx4FcA1b7IlRDEf/Pv0T/9S78n30Xlc1BrYIq98Gshah0DkrdMsZKyQRFNNZxe6noPTcYDNMWZU39zTC1jDuS/PTTT6OU4rDDDhvT+ocddhi//vWvefrpp8d7KAaDwbDj0DqIvgVGPyYKZBgPnhs7UVOSyGJAaLoV1cPpsB9wcL/QDL6HtfcR6LXLxVhqzm7yWjofm3EpJSJNWYHwA10rDU7ATl6dDXR8nsZorUUEu5Wgb3IQOFcJV2o/iJIn/17D87UHtIxSSmqSLUtMujwXZWele3K2AG3z8Z5/Bl5ajnPmhbD3AahaBZrbUeksutwv+7dTQ1/xJlsxM3PG2WAwGHZGxi2SN23aBMDcuWMzoAjX27hx43gPxWAwGHYc5V6JtIEYN+UaduzxGGY2vjv4uaCfr7KcqO9xiFYKFRhG2XMXo1NZVL4RtXj/4LNZFlOxfJMI5XJPvLJXg1QG3dclz2cLg/dt29LqaiY5L3euD/o8p+snFKK2TcTW31rL+DhpsAMBqwfUEIfrKFvGM8gcUJYNysLZ+zDKDz6EffwZWMeeGi9f7oVSdzCHEUxoENYtJ49HgdLyPqcmt7bdYDCMEzW1xl1m4n3qmbAWULVabfsLDYHrunX/GwwGw4wkmR5ZqxiRbBgfTjqIePryv5ORFOBsEcp9UItFsg7FFiK8dNJ1Nfm5DFOr/QHiL4i4Kq+CHuhwLQvIMVga/BowQ4RyuS/ug5wUokPhu3KrunE9cDj+g1Dors2olvbgoYhqrTXZ97xfelAnL2gTKerKdtDaF6ENcfQ/PC7LlokMc0FsMBgMO5RxZ7jPnj0bgJUrV45p/RUrVgDQ1raLtpkwGHZVfA96tkLXpjgCO5OJaj0H3DcYxoKyxBCs0CL/W4EQW/8y3oO/TSxXL/y01hJlDnsAD0yP9j2o9g3eX60UCzfqI551zKRIciYn59vXGYyLpi6NORybdJ66c7VsEcq5RhGsucbgdQUaMUgrVdBdm9HdWyIHa6UUpPNoQK96Br3mOfTWtXFP6gBl2/UCXA3YtxHIBsO0R6mpvxmmlnGL5H322QetNQ888AAdHR2jWrejo4MHHngApRRLly4d76EYDIaZRLkvdnXt755RtY5DkmuQi+lco4kiGyaGoAY2eXXk//V3qMZW/Jefw3/hb5BvDhya+9DVkqT25hshnUO7NbRbpU4ahs7VkWgkEL5K+i8TCGQrrlOOyDXNLJHcMg+a2qUfcbE1MQkQiuOsjFUmB42zZUKioU3+fkOxqqzItTwcR6U1dvt8QMsYd29Gl3vR4XhtelnGOKwr91zZYxDl14BKZ+I071C8205d7bnBYDAYdhzjFsknn3wyANVqlU9+8pOjWveKK66gWq3WbcdgMBhmJErJRXc6a6Z8pwrfkywEb2zlPjMN7Vaxjngdavf9UQv2gLb5+MufRHdvDYRdCgotKCeN7tyE3vhy4JQsolhni+hqSQSb56K1xu/rxl/+FH5YJwuiBPt60LVKIBaDyKozQQI5bDNVK0/q5JhSltRlOxkx0MoWpPbastHaRytbUs8rfehqP7pWRvdsxV+/An/DSnTPFvSmVXgvPIa3aTV0bArcsSWF3X3gfrxNW1GNsyDbgPI9ca8GmUxwUjJ+pR6ZxPB8Mf5SYX23I5MRti3LWxNWAWcwGAyGcTJukXzBBReQz0sN0zXXXMPnP//5Ea139dVX87WvfQ2ATCbDhRdeON5DMRh2bfygn+lMichmC4ELtCXRHCMsDaNB+9LWp9In0bqwr+9OivZcWLcccg0oy0Y5KayWOai2uVipFMqtoiyFwodyL/qpvwSR6BQqFRhX9XXBxpeh3I8OXJqtQiN6xVOoUi/K98UYzK2AZYnoc2siDMt94/tuCXoPa63RfZ0iHMPbZFKriHCt9EGpR4Rqxwbo3AhdGyMBqyr98v1Z6oat61BuTVo/KSXj8fLf8R++E73iWXRPF/6Dd+CtW4ez38EoJyXjHhig0To/So8XB/GKTObYNkr7UvvsBZFm3wfXlXVrpXpDNYPBMG2RFGg1hbcdfca7HuMWyXPmzOHjH/94VHNz6aWXcuyxx3LjjTeyfv36umU3bNjAjTfeyHHHHcdll10GyAfsYx/7GAsWLBjvoRgMuy5uDXq3QF+HXOTNBKFs2ZIC2TRb3KANhtEwqL/vTh5Nrgb9dMt9kvYbGnUlo7uBIZfWGtrmo5c/BbYdLxvWwvZ3R8Ze2vdR8/dAFRrrDL2UZdWP78DxHg2VfujrQPd1wIYV9R4E3iRPbiQ/F9pHeVVUU5t85zgOyRplpZRMHizYC1IpmXhxK6iGVtScRdhvPBdrr4PRTz2I7u0m+0//MeSFq7IdSYMP+2ZrXe9WXdduakCx4XjG2WAwGAwTxoTk9lx++eX87W9/47bbbkMpxYMPPsiDDz4ISJS4WCzS29tLpRK7bIY/2m95y1v49Kc/PRGHYTDsutQSF51uVWp8TXTWsDNjOfXmRzu7WVomJ+m5tQr0dUNDCwC6UhJRBnFLIqXwN7wCLz+L73nYrz5JlvXcIKLrizgOzcBefg7dvhsq3yCvB8uqcEzD2txKv2SAjPZ7JRTFWkM6I99RoYv2UP2CJxBt2aB14PqNRLPXr5DIcmMbtLRLSy1lobWHyhVRlo3ON8lxlnshV0A1zyYU1Oo1b0K1zJWocMK5OsKyUS1zpcbb96FjvUSI843BQVHviVZn3OWY722DYQYw1WZa5mth6pkQkayU4tZbb+U///M/+cIXvoCfmI0ul8uUy4N/RCzL4mMf+xif+cxnJuIQDIZdm4QrLRCk7pVNhNaw86IUFJoli8K2d/p6TmXZ6Ll7irhLZeVxtQwdm9ANLahis9S1+i4oC+uIU2DJATB7IWTzIn7tlKQDK2klpb0abFmHetVx6FxBdJtS6GAstZMRIR2msvuu3B9o6LUdtFJxlLpWhWwhit9O+oVftQTVkhyDk5L67XKv7DtXjI25AKUctFaSag6QyuB7LlZWBH0U4LVticQrG7IN0fYA+RwGEwkKBbaFnrVAMnzCz+pQ55wuyPMzyRjNYDAYdmImbArXsiw+97nPsWzZMj7wgQ+wZMmSIZdbsmQJH/rQh1i2bBlXXXUVtm0PuZzBYBgh2h8m1dRMOxp2clTgwLyTC+QI3wvqWF2JKtsO5IvyHeC7wf+epBWns6i5i1CZrCyXykjkw0mjbEdEnp2CTBZVaAjSr3Uc2VVW8L0y8HtklN8rnovu6Qj6NatApAfb2Fbf4gkjCPdooNQXp0DD4H7RgK721z/u7Uo+kqydSmIZa8A1jPYHpUsHnasl3T15XCHpHKSCvti1srzHBoPBYNihTPiVxV577cU3v/lNADZu3MiGDRvo6emhoaGB9vZ25syZM9G7NBh2XbQvZjz+gIuqVGbU0R6DwTB90Z4Lm1+OBJhuno9e/Wz8t988B9XQGq/gBaK50ie1/3ZKyjDCqLDnors2yvdH4MyuGttE9Gkt625di25sQ2XygJbJiNE4XNcqUO6RemeAakmitxEqTrueLFJZOedSd9zWqdgiNd6+TCrooAey7umAznX4TbNRuUbYsh573iK078l6lRL0dg6doRMKY78GpS6pSQ4nA/o6ZfLCsonEcXJuoFoSER+WzdTKEqG2d5HJH4NhBmIphTWFOdBTuS+DMKnfwHPmzDGi2GCYLHxfzLrCi7PwCzSdl3Q/g8Gw8+BW6iOUld66yTHtVutjsmH0FEQw2ylJ9w0Jsk+s9iXB+jXpBRzW74Zpv14N0NL/exToSglKXVHUWmuNLvVCthgEkJW0lbImtyY5ajWVb47NyLJ5VKaArpUlgh6J5yZ090bo2oTu2gxuEJUP64SzRUlT70tEl7Uf18VD/biHuEE/eGXF5xt9byeOkyCaD0F6vBHJBoPBsKMw38AGw0zFrQ52Qc02SJ9eg8Gwc5HKidD1anE0tGuTtGaCINqbIDl5FhpwZXJxqnA6hyok3KyDCHHoek21FKezO6PLSvFXP49e8QTYDtYeB6MKTVKjO38vMRkbOLE3mXhSo62SYtzJoEs9Ilwz+eg4lJNCZwviZp1vRHdtAazBx5lvqH8cCmCQ/wcun8lL3bJbg1Ty9YEOXsSPTW2ywTCtMcZdOz+TKpJLpRIdHR24rsuiRYsmc1cGw65HMspgBcZFO7vDr8Gwi6IsCz17iUQcg7piFh0orsmWI07Ulh0ItuDqzQtql0MBlymI4FUKbAfl+2g37joRSTcnDXYaGtukX3C1VC+2hyOIFivbhjmL0Btfwd/wMvYeh6BSmbh+dyqv9tJZqJXjNlhKoZ2MtMvToF0XojR1jWpuR2UknVo1zZK4bhQp1lJdnJw0UBagRPhH9ckDzi8bpJi7VQgdr7c1BnbKXBEbDAbDDmZCRbLWmltvvZUf/ehH/OUvf6GjowOQHyXXra+Z3Lx5MzfffDMgdcwnn3zyRB6KwbDzE/birATOqtqT+sNcwzZXMxgMMxNlWXU1vMqyIN80YKlkxHSAqA0cniOyBVQt6BVsp0XEWZYIbqUkSh2aApZ6pLZ5W+LNraKq/ZAronJFvP5uVCorKcZeTaLYSolJ1VSZrfk+oKQ+2rIlgpwrQjqH7g96yndvlu/Tcj+qRUrEwrTzur7FtoNSVn22jlKSit7XGU9Q+H7993A47l4t3pbWQZRbQSGokdbSfguvJq+Npv7bYDBMKUqpOPNmivZnmFom7Ffqueee413vehdPPfUUEPdBHo5Zs2bx5S9/meXLlzN37lxWrVqFNdm1SQbDzoaTgkryiW3/3RkMBkNEYNgVYQ80pBr4fTJUejDoWhVWvwCNrSg7/h1XLfNQLe3xgl5gGuZW6o2tJgutZeJQWSi0CN5MXnoipxyU5aDdamDs1SstxSLxKj24VbLeOHT71jrR81hDfxcSltbDfwU7GdluaJwWXiO5VdmuXxvQM9p8lxsMBsOOZEJU6bJlyzjmmGN46qmnxJxDawqFAoXC8OZBSik+9KEPobVm/fr1/OlPf5qIQzEYdi2UkhRKlFxgpXP1kQ+DwbBr4Q9uQTRm0rnYkTkQlwPRnod/+w34d/wY/+ffQFeDWbtUBmvR/qjIRHCAINaD2y9NOOU+aYUVtsGC4BrFl6PJFaFcgkoZsLHyQSss7aOCVlpDjqQfRHy1luhvdC5BS6tc4+AJAKVEKIdeEmHrvrC3dbQ9HbT3MlFkg8Fg2JGMWyR7nsfb3/52Ojs70Vrzute9jgceeICenh4uvPDCba579tlnR/dvv/328R6KwbBrksqIiU++CXo7oGMddK6PL+QMBsOuQalHHO97tgzTO32UWLZEVxtah2575PtQ6sI+8W1Yr3snaIV++iFobpf1wvTwQnO9477lDCm4J5wwcg1oy4rTnlXgct21FRpnQVMb1pwFoKSjcWi+pb2BE46B8LVsEbt9HdJWy7LlXJWKUrmHxLIlrd2yRQRnizI+oSBWyHayRVOTbDBMc0Lrh6m8GaaWcadb/+hHP+K5555DKcWZZ57JzTffPOK06QULFrD77ruzcuVKHnnkkfEeisGw6+L70Ls1TuXzPan/M/XJU4vWIlR8T/qzZoa5WDYYJhrfD9oIAWgx28pNXjRSl3rQHeulN7NSqLmLYPY8WLh06BVsRybytB7aAXoyCN3AAVVnrAW6txvd1w0FiR5r30NZdlT3p0GO0/PAcaLnlO/FPagHEpqlDYuub3kVRK2j2mQIWkppc0VsMBgMO5hxT+X+/Oc/ByCbzfLNb35z1HXFBx54IFprXnjhhfEeisGw61EtSz1cqXvwRVvigtAwRdTKgTmPD9X+7VwwGwwTiApSfZUVRGknMVLrVsXsCuIeyFpjHfUmrL0O2cYxWnFP4qkgnUP7HrpSQntefUW1Asr98bLVMoNixsnIMyp4zo5dxMP06CSeO/zf/VDnneydHOJWBy9nMBimFSaSvPMz7l/RRx99FKUUxx13HLNnzx71+m1tbQBs2bJlvIdiMOxauDURx7WKXFQFbV1QgePtcCl/BoNh5uPVZJLMDybHPDdO+bWGEF4The+Jk3Mmj1Vowu/eCqUeVK4BNW/3ydnnWCn3Bl4NWVG9nistqrQP6TRq8d7SzkkjordSClo+BXI6eWUa/m8pKXFBEawoNx2s5/tS9jJUXbhKuJPrwEjMdwd4dKlgss1MsBkMBsOOZNzp1ps2bQJgyZIlY1o/lZJ0sIEtogwGw3YYWHOsbLAtyBfqHWsNU0cqG0eSUtn6XtYGw0ThViWtHyRjIdsgE2Z1TLx5n+7vAanajSLICl9aGE3HHu3aj44TJ4XSGu2k0G5NWjkpBfiSTq0stONItNh2JIU8NNiKUqED8ay1lLKUeuJ0aRSkg/7J/jAp0zpIgweiVlFag5UQ5YljNxgMBsOOY9xXcJlMhmq1Sq02NpOQUGS3tLSM91AMhl2LVBoqjkQiLBuKzVNjhmMYHqVMHbhh8nETv7daD52e62QmdJf+S0+gX3wcAOuwk1FOCmXZMHfPaVva4a94DrVkbyCoSQ4JjzcpRLWPQoy7VF302AJ0kGYdPJ8OJsCKLSJ0O9bJc6EwtoapudYJ5/Fw2XQu6NHsxs8py7hbGwzTHGUplDXE3/lk7U+bfOupZtwiub29nZ6enjHXFD/00EMopdhtt93Geyjjoru7m9/+9rc8/PDDPPLII6xZs4ZNmzZRKpVobm5m//3357TTTuMf//EfmTVr1na3d//99/ONb3yDe++9lw0bNtDc3MyrXvUqLrzwQs4999wRH9eNN97I97//fZ588kk6Oztpb2/n+OOP55JLLuGYY44ZzykbZjrKkos07U+dEY7BYIjRvkQTw8yBTF7+Dst98rxli3jyXXF09r1EBkjCwMr30UqJ2V6lJBMtze2xWBuIk45NupQ14LGScosJFq565dPRff+5h7GPfsu2v3d8T5yfw+8nJx2Pj1eT/sUaaWHnpEQglnvRlT4pIdEaalVomo0q1k+ia9+Dzo3o7s3Q34Pu7YQVy8Cy0RvW4//9Kdz+Cs7cWVjzFsDe+2Ef8UZUVvavCk2BwR6xUFaWCOSw33F4XpmiZOiEjtjhGIcoJefmuXHmiOUMX38c9F+O2veFEfh8UyJqbQoQpxXVsvx9Wba8Z5NVymAwGKYV4xbJxxxzDC+88AIPP/wwGzZsoL29fcTr3nHHHaxZswalFCeccMJ4D2VcPPTQQ8OK102bNnHPPfdwzz338PnPf54bbriBU045ZdhtXXnllXz605/G9+NZ6g0bNnD77bdz++2386Mf/Yhbb72VbHb4lNhSqcQ73vEOfvvb39Y9/8orr/CjH/2IG2+8kf/+7//miiuuGOWZGnYqlJIIh8FgmHy0FiHreyJ+kyZNtbLUqlpWnPoc9sLVGnRtcO1piGVJdNhOQSEQvNVSXL86ECclosr3YmOpQnMg1FKTcxFfbIHOjYCGYutgEe77UgMcimLtE08EIOMQeidU+uLzr/aD0wRuFe1WRSBD4LFgQ+d6dL4JlTynUo+I6WoZGlqw5iyCJftDtQKWhTrkeayOLViL90S/9Cz+o/fjexbWG89BRe9Rb/z9mcpAOoeyU3KMbiV5YqCc+JgGohQ0tgWTFCpo7zTMpVWYaeLVgrZRdv1r5rt8ehH+vYft1HxPPhvG78PA1JtpmXmzqWfcv6Rve9vbAKjValx22WUjXq+np4ePfOQj0eN3vvOd4z2UcbPbbrtx/vnn85WvfIWf/exnPPDAA9x3333cdNNNvPOd78S2bTZv3swZZ5zBE088MeQ2vv3tb/PJT34S3/fZc889ufbaa3nooYe47bbbeO1rXwvAb37zG9773vdu81je+973RgL5ta99LbfddhsPPfQQ1157LXvuuSe+73PllVfyne98Z2IHwWAwGAyD0T6Ue2IH81LP4DRn7VMnfqE+vXbI7YYOyUrqZAGVb2K7NcW2I+IuLLGw7FikTwLWIa9F7X4QaumhWPsPkcVU6Q36EmvQXjwWYgkty/huvUBOMtQVoGUntiFo7cuEgrKgsRXVOAsyOVQmh2poRhUasV51FM7r3oLaYz/sk99O7dA3YJ16HipbiM0Niy0yCZFvlAmHcOwGCdwRXJmGadPpbDBpsY11wsjzNE1RNwQEmQ24laFdzA0Gw06P0nr8f/mHHnooTzzxBEop/uVf/oXPfe5zpNNp/vmf/5mvf/3rKKXwvNhk6KmnnuL888+P1nn961/P7bffPt7DGBee52Hb2/7Ruu222zjzzDMBOPPMM/nZz35W9/rWrVvZY4896OrqYtGiRTz66KORe3e4jzPPPJNf/epXANx9992cdNJJg/bzxz/+kde//vUAvOUtb+HnP/953bFt3ryZww47jFdeeYXm5mZeeumlbdZ0d3d309TURFdXF42NjdseCIPBYDDE+F7Qg7gUp0oPd9GcLYrQ8lxJuR5OLIXpvDrom6v9OJMXRGilsoGL8gyhvyt22Abq+i2FQ+WkAyEd1vwCWJBvFFOwar8sXKvGGyi2onLFaDe6ezNU+tG+L6LXsmQzvhfvaECUVjtpVKYwsvPQWoRRGNlPZUwIZ1ek1BNHkAFQ8nnIFsznYRKYSdep4bG+cOjeNGxHN0wkPZ7HXo8/PyPGaGdhQqacr7vuuugN+8pXvsLuu+/OP//zP9dFW7/yla9w2WWXccIJJ3DooYfy5JNPAtIC6tprr52IwxgX2xPIIFHzffbZB4B777130Ovf+9736OrqAuCqq66qE8jhPr7xjW9E+/r85z8/5H6+8IUvAOA4Tt3yIW1tbVx11VUAdHZ28r3vfW+7x24wGAyGUVKrQF8HlLoGu8lHtaxhayBbIk/93bJsY5vUL9atQ7xO2ALI8yAwi1Jh/p7vT//I1cCJgmRrI6gPwGpfosu1EnHrpGChdA7dsxU61qK8GspzJbXacqQuOyGQgcgdWtk2KhTIEL0P0rM5cRxaRxH6EaGUTFBki0HrKCOIdkn8AR1XHCOQDYZdjQkRyQcffDC//OUvmT17Nlpr1q9fzze+8Q3uu+++yHjk3/7t37j66qu577778H0frTXz5s3jN7/5zQ437RoNDQ3iXFsulwe9dttttwHQ2NjI29/+9iHXX7hwIW94wxsAuOuuu+jp6al7vaenh7vuuguAN7zhDSxcuHDI7bz97W+PJiZ+/vOfj/5EDAaDwbBtaonv+aQTshdEl5WCXKPU6NatF9S0OunERbWSbSS8KiLDrrrWPxp8V56fjmgt9by9W6Vfcjh5YKfiuuhBPX5D4Rw4RVuO/K+DHsN9HeDVJJUa5Ny9KmxZhe7dOnj/IGM7wLFaY9W3XtNa2hdbo3CK1jowXusOaqyn+WSFYXJIusOHkybT9W/SYDBMChNWvHTCCSfwxBNPcNFFF5FKpYLZ3KFvjuNw4YUX8sgjj3D44YdP1CFMOs899xx/+9vfANh3333rXqtWqzz00EOAmJml08P3jDzxxBMBqFQqPPLII3WvPfzww1Sr1brlhiKdTnP00UdH64y1BZfBYDAYhqGuDY8FVkpSgUMX4lxT1FO3rpbVskVkVfqCWtfA5TpTkFTeWgVqZRGD3VvASRPIRbRSaMsZQmhOA7QOarGDSQDtyXn2dUiEt1aWOu1aRY5fQ5xanRAYvhsHkyuBQZnW0NeNrpZlH3ZaJiBKPejkxEJYO6yCiLT20dUyuq9TeiEn9qNB3q9q38jP0XPjKKLvDUi5NewyZPLy+cs1Dm+gZ9ilCec3p/JmmFrG7W6dpL29nWuvvZarrrqKO++8kwceeIC1a9fS1dVFoVCgvb2do446ilNOOYX58+dP5K4njf7+ftasWcOvfvUrrr76alxXfjw/+tGP1i33/PPPR3XXAwX0QJKvL1u2LDL0AnjmmWeGXG647dx+++24rssLL7zA/vvvP6JzMhgMBsMISGVF9Pl+UG9sS8TTDWpmq6U4HThTkAip9kVEhhFWL4iY1spoJx1FQJWVku01tMi6nic1uUpJPWzTnKk/X62DKLgPTnawCVi1FItGpUSF+l5Qx9sTOGsPcG3WmqjXcPQ4ecWnoXmuRO7CWu5sViYVQMY9SUMrdG1GKYWOeg1ngwkNHQd+LVvkeSaHHm269bYeG3YdhnMpNxgMuwST8g3Q1tbGOeecwznnnDMZm590fvCDH3DRRRcN+/qll17Ku9/97rrnVq9eHd0fLkU6JJlevmrVqgnbzvZEcnd3d93jTCZDJjODjGEMBoNhKgnbDIFES1NZdDIFt1JCKyUGUhCU26p6Y66ku7XviwhN5+J06qi3ro1Slgi6lELtCPfjaimOErs1cX0eiqgO24qNzUAEdDqbOGdfBHMY9Y03MGBzSsy13Co6k4vGRCkl9l1Jodq5Ud6DdC6K8MmwK4lKRw7fiXWc4TO7BmE7sm23Goh+I5QMBsNgIh+JKdyfYWoxHdFHwSGHHMJDDz3EZz/72UEf1mRtcbFYHLhqHYVCbObS29s7KdsZit12242mpqbo9tnPfna76xgMBsMui5tIefZ9dCVwuNa+3LIF6e+rNbqvC7o3o3s2o91aVF5Uh7LASaN8TwyqtI+qVaHchwpSs1UqjQqjqhOB79fXU29z2YQ5mR7CPCyVjVOdw0mAOsGbSK0elCeo6pdNvKZ7gvpmy0KlUpEplwZUJo8u9+P3deP3dqFLvfL761aji1StJapPzxboWBekegfHbzmo0YhkEFGdLRpna4PBYNiFMVOkQ/C2t70tqpUulUosX76cm2++mZ///Oece+65fPnLX+b000+vWydp5LWtemSgLnpbKpUmZTtDsWrVqjrbeBNFNhgMhm2QzUNvEFlN5+qNvECCo04KNqwUXy7bkQikW5WIZKUMTXGXA5WMpmoN1YpEa6vl+hRlpSam33G5V9KXQUy1ttdSKpWBSiDOnSEEomVBvlm2G9btRkJZJ/pEa5kQyBWh0h/UcFtxi6ygJhvA7+2EalnS1pWK+z4Dyk7h/+0+9N0/hZT0H9Ynn43d0ia1yJUSKpOThSv98fHUqjKeTlomMgwGg8FgGCVGJA9Bc3Mzzc3N0eMjjjiCc845hx/+8IdccMEFvPWtb+Xaa6/lwgsvjJbJZrPR/dB4azgqlUp0P5fL1b02UdsZisbGRtNbzWAwGIbDc6GvC9AiePs6JN22cTYqlYGKE7UgErMuG1CSopxvAj9h8uT7kG9Aax2nDaeysn6Yjl1shUxORGS1VF8DOd56yNClOaTSv32R7KRj5+nh6niVErFb7pXJgLAmOJOXdZSKRXG4bPIxBCnRDrrUjUqlo1ZLWmtUODaA39uBrvXCgYejWufAwqVYxWaoVtCdGyBTEfHs+wmDLRX3mU5v/3fRYDAYxoJiahNNTE7L1DPiX+FXXnllMo8DgEWLFk36PsbDeeedx69//WtuvvlmPvzhD3PGGWfQ2iqtP8LWULD91Oe+vvjCZWBK9URtx2AwGAyjpNQj7ZdARFfYzqi/E5rnorIFtGVH9arKSaF9H1VsAUD7CWEZmUkhrY3yzSLeGmZJJNVOyWOAXIMIVN8TcWynhhepo0FZcar1SGucRxLBDsWvG0zk1rW6AtSAqLgaYt9eFXwXFUwGaADXRWcKUptdLcEry1CZLLQvgMZZWC3tsqyTptrRTWbO7tJT2U6hCy0yKZBKy4SGnTKp0gaDwWAYMyMWyUuWLJnUonGlVOQcPZ1561vfys0330xfXx+///3vIwOvpMlW0nxrKJJmXQN7RA/czrZaZG1rOwaDwWAYBxr05rUS9cwWo1l8lU44LwP4cUYPtiNtkVLZASI3YcRl2YNbyoTR2IlEKSi2SARZqYlPO1Zq+5Hp0ZJKxyZo1W2XEGXmzY+dxQFFauLH0GAwGIbBGHft/Iwqn2uQCckEEJluzBBmz54d3X/55Zej+3vvvTe2beN5Hs8+++w2t5F8fb/99qt7LelQPdLtOI7DXnvttf2Dn65oLRGUyAXVYDAYdgC5BujvAq3xlz8J61cAoNJ5aJkXmGkFbYZqFRFlnovftRlVaBQTL19jtS2C3q3xdpMu10m0L62fwu89rSWnznIm5rvQdiA/wSU24TFrHfeITqZTay0TBcqOH3uupKYrK3jdR3vSg1iH7tHaB3x0qRcqJfwtm2DF8zB/EZT6UJmsjHO+IUjzdsSBG4I8RCW/IaGpWlgrbY9hLINjrEsRH9HyinFlAIx2v4apYaLeX4PBMKMYlUgOZzEmUtTOJIEMsGbNmuh+MsU5nU5z5JFH8sADD/DAAw9QrVaHNd665557ADHOGhgpPuKII0in01SrVe655x4uvfTSIbdRrVZ58MEHo3VSqdS4zmuH4ftyMal9SfMrtpofIYPBsGOwnSAduhoJZAC9dT3M2x3KfVFLIl3qRfd2oObujtU6F10rQ74JSykR2pYVC2PLgkoP5Brj7zfPhd4OeS1c1q0Bgfgszpp+Qslz5dxAJjbdmtT9hueQKUitctKoq6dDTL6UJT2Oq/3ge1J77GQkrVr7kMrKuHauR29eg/77Q6i1q2DFs7D7XmjtwoZXZPwXH4ias5ukwfte7AQeRqHDiddQ1OSbR26EpnV8DigxH9veb1KlP66JzhaiNPtRoTWUe+J69ewI9muYfMLa/tCoLls0/ZMNgprir+hp9nOwKzCmb+ADDjiAq666ilWrVlEqlSbk1t/fP9HnNinccsst0f2DDjqo7rW3ve1tgPQj/tnPfjbk+qtXr+bOO+8E4PWvf31dDTJITfLrX/96AO68885hU7d/9rOfRX2PzzzzzNGfyHTBrcQ1c74vF6cGg8Gwo9AaaiWs496K2u8orENei7XHQSKQtYZaRYRdJodqWxAJYZXKxldMyVZKySixmzD2qpUBHfQaDr777MBwSimJVE83quWE8LchnUi39n3pUxx+n2s/MD4LUsltR8TkwLFxMlLTHPaRzjWg8g3YJ7wV67yPoZYeDBvXx+OYb4SFe8fXi15ie0nC9Hbtx72fR0LYPkpWrn/PhkInTcMY+2+YV6vvqT1RLcAM40P7sUCGuA7fYDDs9IxYJLe3t0d9H5955hkuvfRS9txzT8455xx++9vfYlkWmUxmXLcdyQ9+8IO69ktD8aUvfYnf/va3AOy+++4cf/zxda9ffPHFNDU1AXDppZeyZcuWutc9z+Of/umf8IIf9f/4j/8Ycj///u//DoDrulxyySXR8iGbN2/m//2//weIE/fFF188klOcnlgDZmTtEZrLGAwGw2TgVlBeDWXZWLMXQKFRsqiUAscJnJhtEXTKqqsTGzTRPzDMEEQGtVdDBy2LtNbSj9lJiaCEiUu3nkg8V+qE644rbP0UiDt/oLCz4nRny4ojo0G0VGsdiNlgXMLX8o2QK0jP6BNOR73ro1hHnY510IlYh52CCidXE07YKEsETPL3UgUp3v4I+0TD4Ijzdg3PBoSTRmqQNmi/A9YzUeQdi++JkV/SIR7G/v4aDIYZh9IjzHf2fZ8//OEPXH/99fziF7+IBGV4gdDS0sLZZ5/NBRdcwFFHHTV5RzxJLFmyhJ6eHs466yyOO+449txzT4rFIj09PTz11FP86Ec/4r777gMktfo3v/kNb3jDGwZt59vf/jYf/OAHAdhzzz35xCc+wUEHHcTatWv58pe/zN133w3Aueeey49//ONhj+fcc8/lJz/5CQCvfe1r+ehHP8r8+fN56qmn+N///V+WL18e7e/973//Ns+tu7ubpqYmurq6pmcLqFpFLm6cDKS23RvaYDAYJpVaRdJeQ0JDyVQ67nIcpBorkHRjJy1R4Fo5UZM7hMjNFNFo6NwgF9uWBVgoO6jfTQq+VHbijbHGQ9em+hRmFQhkIKr99b16kampf2w7UOoLngtqhp0UurczPlfto7euR7XOjVZTLfPisenZivbd+hpv35MxjFaw4ui0UnJ/NO2gPFciu6HT+PbwvaBO2hrs9D0aQkf1ke7XMHn0d8VZDyrouW2N8/01DMu0v05NEB7ryiP3o9GZukmTbtdjyUPLZsQY7SyMWCQn6e7u5qabbuKHP/wh9913X1RXHArmvfbai/PPP5/3vOc9076tU8iSJUvqjLiGY+HChfx//9//x8knnzzsMldccQWf/vSnh623Pu200/jpT39a1xN5IKVSiXe84x1R5HoglmVx+eWXc+WVV273mGfSl4/BYDDsUNwa1EroMN3V91EosINop9YonSgNyeSh0Cx1rGGK9DD1rzqVhY4NkM3FIsj3pA1S+HsRml1li3JBPl3oWF//OBK64eOUPI7Ms6gX/iDnXO6t24YGvCf/ir37PlBojCJ1urcDVWgSMzXPBctGpXPQvSmOOEPwmlMfeLVS9VlJ023CwTD96euM0+6VJX/jhkljJl2nGpG86zAmkZxk5cqVXHfddfzwhz/kpZdeko0GP4pKKY4//nguvPBC3vGOd1AoTHALignkueee4ze/+Q333XcfL774Ihs2bGDLli3kcjnmzJnDIYccwumnn87ZZ59NPp/f7vbuv/9+vv71r3PvvfeyYcMGmpubedWrXsVFF13EueeeO+Lj+vGPf8wPfvADnnjiCTo7O2lvb+f444/nwx/+MMccc8yItjGTvnwMBoNhh1HplxRLQNcqcS1ryzyUnUJX+gCN8lzwPLRSqHwjZAro3g4RvKEoDM24dFB3bDnSY7lzA+Qbpb8vYTw1EZXVWtKtcw3TK2LVvTmuk3VSsYt0iOUEJlni8i2tpxIRZ8uGTFHGtyaZaFpZKAX+hrV4995B6uz3RpvTqRwqk0OvXwFdG+TJhjZxt45MRH1Jpc4UodqHCkQ3VkrGN+zRnC1Mr7E0TH/cajyhM90mrHZCZtJ1anisLx899SJ58YNGJE8l4xbJSe677z6uv/56brnlFjo7O2UHwQ9TLpfjzDPP5LzzzuPkk082/b6mkJn05WMwGAw7jJ4t4LmSBZRMuc4UUK3zpT3RllXQ2IaybBG4+SZ0X6eIP6UgnUdlchKRTpr8NM2RVOsta8B2pN8yQConpb1hX2A7PfE9jSeCSp8Yd4G4WGsvFhGZgtyg3qisrmY48ZuvdTCp4EbXAtXvfYnU+ZegsjlZNtcI/d3oNc8TTSDYDrTOl8g7oNN5VK4oUf+O9SLanTSks5IKb9ky2WAwjIVkdodhUplJ16lGJO86TKhIDqlUKvziF7/g+uuv5/bbb8cNarrCH8P58+dzzz33sMcee0z0rg1DMJO+fAwGg2GH0d8N1RLa96ESpwVrz0d3b8aavUjSq520ODsrC71uOSrp8pwtQDoP/d0S2QxqePWWdejNq1G7HyQiOqTUA046iHxakGuqd42ebLQWge67gS9EsG/PDSK+CjI5+d9zRRj7PmTzEhnv75bJgExeoscgYlb7UC2jezultVNQj6zXLIdMFlwP+9ATA+OyGtXP/gf2aedgH/sGVOj0XKugt66LIs+S2t4k45UKJhl8aR+olZKJiVQmMFDTMgExHp8L7cv747no8Px9VyZJTGTRYJgwZtJ1qhHJuw6TYp+YyWQ4++yz+fWvf83q1av5v//7P171qldF7thr166NIs0Gg8FgMEwLcg0SAO3YgN60Bt3TgVYW+uWnsdp2E/HnVlGeKyKpYz10boh+2wBZpnszuBW056IsG6UUVtt8yBawMrl6F+xMHtwq2q1JTXPvlqGObPJwq3LzfRHLXmCKVekT4yLflQiyUpJ+7lbluWBCgUqfGE71d4kzuGWjtA7quB1UNi/1xNkCtM3H2v8oca5esAf+upX4Tz2A97sf4pzxD9gnnZ5wrg5Mk5rbpR403ySp3CA1xmG6d5C2rrRG2Q7Kd2Vd7UO1r97gaxRoz0V3b0IH7sYqbNelfTExmyp8XyYmot7NBoNhOqCUmvKbYWqZ9I7o5g02GAwGw4xAKRGG3ZvlcV83FFrkfp0ZlxaxqLUIl86NkG+AYis4OaBLxPLAzTtDOBaHbtHRpic8uWv8RP17I09qYbutlSyJTOeKYKewlEKns1h7H5Y4ZwvVvhhrr0Pqzz1wilaAzjWAV40jxcOJRRXsMz5wOcaBrQV9T262M3yrpa6NIv4HvK7dWpTuPSX0d8Z9mD3XGEgZDAbDFDEp3/TVanXIdOtwpn3u3Lk0NzdPxq4NBoPBYBg7Le0ienu3ShRz3p7Q14m/fgXWvD3EodmyJIKaL0LjbKllzi0QEVztQ+cbo0nhKE23cyN6yxp024L6mmNlobIFdKUkIq/YOrXn66RFfPpu0OYmaEeVzsUR5KB+WlsyiaCVJctrja7VRIS6VRG06XxkQ6ZSaVAZtPZjQ0+SkwIK5i9BNcwKHkpNd9SPOVeUKHTyePs642W1DtpO2fH2wufDx5VeMV4KlwlTxsNt5BqHrjkNhan20W4V7JRM9ucbJXV8qkj2nh7Uh9pgMOwwLBX3tp+q/RmmlAn9pk8ad3V1dQGxMM7lcrz1rW/l/PPP541vfCPWMC0yDAaDwWDYUSjLRu15SP2Tex8R3/dqkchSgJq3Byw+QMRb0Fc1mTWlbEeif41t2Iv2r9+uH5tfqXzD6Pv5TgShGB2Ikx7s6Fspx22ubEfSqYvNUjeMD7nGKMqavJyrzyLT9fcr/RCKZJDI87baNaWyUA1qn30/uHBMbnMIo7CghZTcryV2r+OI8gB05xZU8yxp+VUto/Jxf1w1ldH+TCFhkLb9zhoGg8FgmBjGLZJXrFjB9ddfzw033BC1gEr2TT7++OO54IILeOc730lDg3GZNBgMBsP0RWsNneuh3CdRxsY2/JV/h1IXVvvu4pwctBfSWqPXvAAdG1CL9qs35AqxbHRvB3rDCgBUYxuq2CLtizxXIslhm6RaWW5ORsTydCtRSmdFZFpx+rJSClrnyQO3JqJSKbTvR22ukoTp2lprmVhQA1KhaxWJ0odCNFsQYRxiWUHddLA1bdUrcu0H61rx+CWOF9uJhX4YeS73SZTWckQ0aw/V1Ibesh7V0IxqaEkcOUOKaqnp7pf9p3LSJmsisFMyRoq4t7bBYDAYJp0xieTu7m5uuukmrr/+eu6///7o+VAcL126lPPOO4/zzjuPJUuWTMiBGgwGg8Ew6ZS6xZQKoK9DamHXvYi19xFxZDVsb9S5ETa8LE+9+DgcdAIqlZH2UUqJrrJc9NoXoiizLvehcgVCgSY6TosEC4VfrSwia5qJIgVoJyVSMZwkIBDKQUssalW0X0N3bsKvllFNbdBXAstCtS+QZf1ggiCVgVqiTZbWEjVNtpEq9cikQfhcX2fcrxlEAIe9qOsI3iNUveGVnZL0a9+T+76bqPmtisgHVL6IyizBf+J+eNVr5Py1LxMFmSFadNVK0XtMtR/sYdK4R0upOzAiQz6Xyai7wWDYcSg1tROZ023SdBdgxDnPvu/zm9/8hne9613MnTuXD37wg9x///2Rq2dTUxPvf//7ue+++3j++ee5/PLLjUA2GAwGw85J0LqoDsuKU4W1pj4NGKlrbZpNUJk78whFYJh2bFmBYI4vJVQ2B7kGrPl7Qq2GapmLWrg71rzdZHxUIhqbytanEHtufV/lsV4UDlwvPO4Q25FJCK86+LWBNM2CdE4mP7JBP2hzsWowGGYY1WqVr371qxx33HG0traSzWZZuHAhp556KjfddNOQ69x5552cdtpptLW1kcvl2HffffnEJz5Bb2/vkMvvbIw4kjx//nw2bZK2B2HE2HEc3vSmN3H++edzxhlnkE6bvoEGg8FgmMHkGqVOttwvjtUNbTB/Kf6Gl7HmLq6vGW5bCNUK9HWi5u6OygUlRbkGSeG1bMjkUfP3Qm9YCam0RFK1lr6+aHSlLCJMEWTzKomcWlNoDjVSlCX1uOFxAloFgt/35RyCSCyAshNRZyyZOFAkUswREzAQsVrpCwR3EAVWCjLFelFaaJZostaybGTg5Q8RTR4GrSUqG0SYteVApR/dsQGVb0DnG2QZJ4118HEjE8WpHOh+WS+VnTghnWuUaDIqboFlMBh2OFPdtWc8+1q9ejWnnHIKzzzzDG1tbRx77LEUCgVWrVrFn//8ZwqFAu9617vq1vnSl77Ev/3bv0Wls+3t7dx777185jOf4ac//Sl/+ctfaGtrG+9pTWtG/Cu8ceNGQN6kAw44gPPOO493v/vdzJkzJ1qmWq0Ot/qIMCLbYDAYDDsSpRS0zKt7zt794KGXBdQ+Q7hRp7J1dbSq2IIqtsTpw15NYsn5ZijOIBNLJ4Wu9KF8RPiGTthI1Fw0cCA8e7tRC/fcrliM6paTKdShP3ZYt113DGlomhOnZtfKQfo7gelXVtLdo02pQQH9qMdySK0iqdTZAv7yv2Htd4zUl4cCfCQXp5YladwTjZOWiRqDwWAYA6VSiZNPPplnn32WK6+8kv/8z/8klYpLefr7+3n++efr1nn88cf52Mc+hm3b/OpXv+LUU0+Nlj3jjDO46667+OAHP8itt946pecy1YxqqjqcxXjmmWe47LLLuOyyyybsQJRSUasog8FgMBh2OpSSKHMovGZY2q6uluJjj3onJ84leE5rDZ4n0d2kyPRcaRc11HknzbDCWmJlB/2o/cH9jMNtuEFts1LSOiqdIxLZKoxeD1DJypIov+9JDXLoSp7Nw+zdYgM2rQOjsm24bRsMhl2TGdIC6rOf/SzPPvss73//+7niiisGvZ7P5znkkEMGraO15qKLLooEcrjstddeyx577MFPf/pTnn32Wfbdd98xHddE4Ps+jz76KC+//DL9/f2cf/75E7r9cU1hh/XIE3UzGAwGg2GnRqn62tyZgu9JxHXAcUePwxrshPFV1HM5XNay6pYj+btv2ZBtEFEaRnlV0FN5qMsD35OUaSduzYTlSG9nWTnYjh5sgKYUumsr3rOP4q98RnohA9p2sObuPvi4phK3Ct2boWfr9uulDQaDYRvUajW++c1vAvAf//EfI1qnWq3ym9/8BoB3v/vdg15fvHgxxx57LAA///nPJ+hIR88111zDvHnzOProo3nXu97FRRddVPd6R0cHBx54IPvuuy8bNmwY0z5GFUk2QtZgMBgMOz1uTcSakx4c4XQDs6dURgSUW42dmocSVF7gnmynhm4dNFMo9YiDdXgdMFDoK4Ve8wpq3m5xJDgh8pRSQdcmHdQvDzFRYCXqlsNltxtxT1yXDIw2oySyPEAk+91b0H/+eSTGfe2jlhwQC37bkfdyqt8zrUUgR6ngGoqtEiH3anKq2fy0cz3fqQn/3p301E+YGAzj5LHHHmPz5s3Mnz+fpUuX8tRTT/Gzn/2MtWvX0tLSwvHHH8+pp56KlfBzeP755+nvl170hx9++JDbPfzww7n33nt5/PHHp+Q8BnLJJZfwrW99C601jY2N9Pb2DtKoLS0tvPrVr+ZHP/oRt9xyCx/+8IdHvZ8Rf/uvWLFi1Bs3GAwGg2FG4dWg1CX3q/1QaIkvjsMeviC1sJmC1MWCRDCLzfVCzffi+thaWSKlM1Uoh22WdCIKrBTa86FaRq9ZgbXHvugwxdp3xRE6cKzWOqgxDsWzCu4PJJWWNO1IGA8jkJUlwsWtgaMCt+x0fRRYqbhtV3gaq1+EWl9CiCp0b+dgU5yk6/ZUkqyV9n3o7wxSzoMxr/RC87yRm5QZRo3WftSaTFVFLFAtiWncoIkYwy7LDmoB1d3dXfd0JpMhkxm6JOTJJ58EYOHChVx66aVcffXVdWLyqquu4tBDD+W2225j0aJFQKz3mpubaWhoGHK7u+22W92yU8nvf/97vvnNb9LQ0MD111/PW9/6VubNmxd5ZyV597vfzQ033MCdd945uSJ58eLFo964wWAwGAwzioFGUZ4bi2Q/8dqgFlA66L07QCQn8T0Rc+FFykxKuc7k4wmBSknGIldEodF9PWAHUeYwwlx1g57DvpQHRxeUSiKhWidSoxNENcxBNBqduJ9cLnCztgJh7NegUpM65roU8Bj/9lvQPZtRR78OFu4Jq5dDvoh18Iki6Kv9sm46N3LDrolEqdi9O6xfL3fHrwH4MgGBZYxOJwOtfehYL3/3ThqdSsefPM8Dx4hkw44lFKghV1xxBVdeeeWQy27ZsgUQI66HHnqISy65hI985CPMnTs3evz444/z5je/mccee4xUKkVPj0zsFgpD9IMPKBbFpHCgYJ8KvvWtb6GU4lOf+hRvfetbt7nsMcccA8BTTz01pn3N0Cltg8FgMBgmASctLaBC4ycnkdrqZCSaHAq+pGi27MFtm2wnYWil5PVSj0QGLVva+8yUiGAqA9USumerCF7LDuqFPVTLLLRXi0VvWEs8pMjU9dtMnn9Ug+wndLEaOnqnLLn5Xr2xl/ZFKIeUeyXi39eJddhr0Fs3oT0Pa88D0XsdArMXocJJkHQ2EO/BJIBlT31f5GxRPme9HdAfZDSEu/f9IAXcpFtPGrVqPFHmuZKdQPD3Xu0Hq2jSrg0AKEuhptC4K9zXqlWraGyM28ENF0WGuEy2Vqtx7rnn8rWvfS167Q1veAN33HEH++yzD3//+9/5yU9+wnnnnTdJRz9x/PWvfwXgve9973aXbWpqorGxkfXr149pXzPk19lgMBgMhinAsiXFOtc4OL3SSUEu2bdXS9Qx1wj5psFiSlnyWrYoPW61LwIZRNy5lak4ownD37Ie/coydNcWdLWK98rK6CJMNbbUO15btkTaExHZurjuUNHeMCXb90F7IlT9hJFXksgALDDniozAtIjv6KA9SXWvluRx8yz03/6K/8h9qDmLsZKCRwWp4OHkh+9J+v1UUy1RN1oacLLQMEvaX82kDISZhpMimpXQvkxYJD+rtfG1OjUYxktjY2PdbVsiOZku/YEPfGDQ64sWLeLNb34zAHfeeWfdOn19fcNut7e3NzqWqWbr1q00NTUNmwo+EMuy8P0hfkNGgIkkGwwGg8GQJEzjHfK1AT+bll0fbR6IsuIUbGuIVkQzBN3Vgf/8E1gNBdTcJQDYxSZUKObq+hErVDqDrlVEsAbiVyVFdHQ/sROvxpA1yENkWw9MpY6iyflmeVyrRKv5rzyPap0tR2ZZqN33Q+1zqJiHDWTQRMcOEKRDHVcqUy/+DZOCsmx0y1zJJkmlJQ2/PzFRYiYoDDOIPfbYY8j7Qy2zbt06AJYsWQJAZ2cnPT09Q4rRVatW1S07lTQ2NtLR0UGtVqvr9zwUW7dupauri/nz549pXzPnF9pgMBgMhh1NmIJrOZDKDjKG2u662aKky6ZzI1s3dNQeGNH0XEn9DqOsnouu9KPLvSJOt7tdP3Lu1W4N3d+D7u9G93XKrdSH7tiAv3Ud/ubVVL/xafxlT6KVQgU3SGhcJyWK1LIhK32GVahsk+nXkcYI65Pt+HwqgUmSsogWTNYYJ1HBOpGgtCTybztyS+dg0xr8R/6IvuMn+C8/J4In14B14FGo9DYEZ5gm72QGT4oMOZbBe1SrxCZbQy3m1uT9KffJuVZKkqJeKcnjakm2kc5COi/7tlPymTECecpQTgpVaEKlc/L5iv7ezUSFIUFYdjOVt1Hy6le/Ovqu3rx585DLhM+Hdcb77LMP+bwYFz7yyCNDrhM+/+pXv3rUxzReDjroILTWUdr1trjxxhvRWg/r0r09jEg2GAwGg2E0OGkxVUrnRn/hkspI6nUmv/11tZYa5nKv/B/W/Lo1qVct90JfF7hVdM8WVKkHSr3QuV6E2Pa2W+lDl7qhcz30bIaOdbBltdy2rpZU540vw6ZXcN54KnY+hV61En/dS2jto+wUKqyPDZ2mLStRn50wN7MdxNnaj1tr2U7sIp00OVNB/bblDD9O4WSF74NyoKG1vlY3lcH/w4/RT/9VjuOlp6GxTcYegpTuAWK2VhE38rDtT60c1EfroZfXWoR6uTcWuZV+GGLsdaVfxrdna1Bv3C1O1eVe+b/aHwjnPih1S6unxllyXtkpros21BOWWYzl791g2IHMnTuX4447DojTqZPUajXuueceAI488kgA0ul0lIL94x//eNA6L7/8Mvfffz8AZ5555qQc97Z4xzvegdaaK6+8cptp1E888QT/9V//hVKKc889d0z7MiLZYDAYDIbphtbQ11EfQQ7rmZO1zNqXSCSAZUuENNcgoms4fDeOQGsdi8ukUPVqEkVrbkPNW4q97xHYp1+I97e/4a3aiGqcLS2vii3QNAdVbEHNW4pqmYcqtMgxNM8NhGmTREOLLdA4O67RzjfG+3ZSsQBRlrxWaN525C5bgOY5IiaHMFNSh5wQ3FGoVx0fnKMvIrTULWI2KXy9IepN3VowSdElt/CizK3K+1PqHuyI7rt129WlXul/HGJZcQRcBW7fAyPmA7dpMBimFUqpyLxrSm5jnKC54oorAPjsZz/Lgw8+GD3vui4f+9jHeOmll2hoaOCiiy6KXrv00ktRSvH973+f3//+99Hz/f39/OM//iOe53HWWWex7777jnH0xs773vc+9t9/f+6++25OPvlkfv3rX+N58tv1wgsvcMcdd/CRj3yE17zmNXR1dXH00Ufzzne+c0z7Unpg92XDTkd3dzdNTU10dXXtkCJ7g8FgMIwC35M2QGGdbVI8huI1gS73gZOOUoh1IORU2Cc4dGh2qxJBlhcDI6IK2nXxn7oP5i3GKga/EbYD2Qb8WhW7oUW2qzW6qxNrwe6Tc96hcdewzthj2GTXFnBSqEJwXtWyRIhDssW4d3XS4AsAJanPA5/LFkU4Jw3Fku9JmFYfpqRvWVM/AWHZKNsRsRxOEkSXYsH/+SbjomzYZZhJ16nhsa55w6tpTE3d32h3zWPBnY+NaYz+53/+h8svvxzHcTjyyCOZO3cujz32GCtXriSXy3HLLbdE0eOQL33pS/zbv/0bSilOPPFE5syZw7333su6devYZ599+Mtf/kJbW9tEnuKIefnll3nTm97Ec889N+zkgdaagw46iD/84Q/MnTt3TPsxkWSDwWAwGKYTAwVYKKA8N76Fz2stbWp6O9C+L27TKqgGDkRwlPLc3x24N3tSw6x9EWKpNNYhJ2LlmwI37gbIN6H7u1FKiTjWOmj9NDaX0BERtZaauJRW1TQrFsgw2BSrzr08I3XmYb30UMeh/cHvj7wg/w0QyNFz0X0H8g3Bfqg3MZMDkvE3AtlgmN7MgJrkkP/6r//iD3/4AyeffDLPPvssv/rVr/A8jwsvvJDHHntskEAG+Nd//VfuuOMOTjnlFJ588kl+8YtfUCwWueyyy3j44Yd3mEAGWLx4MY8++iif/OQnWbRoUfQbFd7mz5/PlVdeyf333z9mgQwmkrxLMJNm6AwGg2GXp78rTq1ORlV9LxK8OpVBaR90IIx7O9BYqKY2UJa8FuKkRXSVeuJt2Y64PwcRZZ0U4gBOUEMsByH/KoXfvRVr3tAuqTOCyAjNFV2bSsXtqjxXWk9pn+jcnbSkgpd7YzEb9b5OEIptJwOZXP0uXTdI1fYkepxrRPle0E5oiEuwZHTbYNgFmEnXqVEk+eTDpj6SfMejM2KMppq1a9eydu1aPM9j7ty5LF68eEK2a76FDQaDwWCYTmSLUuvq+xJxDOuSw1rkMFKsLDRKjJ8aZ2MpJdHh/h6JVoZUSyKEQdowBVEJBXGf48itWkOtJinKQRQ5ek1rVPPsKRmCSSNMQS/1iBiuIvXAvgeWqhenYfp36HCtEpMGwViH70O8zuBIu6oFfY8tC10tQ7kE6Yw8p6x6B22FmIflGk002WAwGEbA/Pnzx9zmaVsYkWwwGAwGw3TCsqHQUv9c6KTsVsGyI/GqlEI7gbmVUijLQRca0Z6HsiyJU/q+LAtoZaEsq773sCuiWHsehD2O+3sgSFPW2o97Cidra2cqvlcvZrWPCFZbxjlpqmXZIlqT2A7aS5hz+bW4tngIozFdK8tEg7JQ2UKwnhKjsDBTQGvqekF7rhHJBsN0xkIm1qZyf4YpxYhkg8FgMBhmAk5K0oFJ6Cmtg+cS7tAg/Ye1hmopatOkPVfcppUKIqmyvl63Ev/ZR7COfhMEJl1k82II5rmoQhNxze1OcNlg2XILBX/Uwzms+0vMIChr8MSA54qxmluTtHelQFWhuX3oNOlKv7QQClEq3mZSCCcnLky6tcFgMAzilVdeGdXy2WyW5uZm0un0qPdlvoUNBoPBYJjueLWEK3PQf9i25b7tiEu1ysap1In+kbrUI7WwWou7czJ9GtDNs7EOOV6cnAnksGUj7s454ppkSRme8Sgl7ajcaiz6w2hx0mU6lR1+UiCVEVO0almEctv84SO/bg3d140qNsfp7ViAV1/bHNZC54yztcEw3QkzeaZyfwbYffexdVfYY489OPXUU/nIRz7C0qVLR7TOTvBrZzAYDIZdnmopMLyqbX/ZmYg/oNbVSQGx4Zbu2YresjbouTvAsdlJi9jLFsXV2qvJeuU+fLeKlS9CSzsqlZb9hI7L1f4BbtBq54gkg5xXKisTDElBmqw7rpUlSp9tCKLuRNF3GudIH+d8A7RuQyADzFkC6SzarYHtSOq6kwInKxsMI9u2I/2lbSOQDQaDYSgGOlmP9LZ8+XK+/vWv86pXvYpbbrllRPsyItlgMBgMM5tSD3RvlhZHnRt2jrrZgVh2JFg1Cu17QchXQX8v9HWh2nZD5RpA2ehaRdKr+7vEmbncC70dEq3UGjo3o7u3RO7YkmXso7QHvivGYeksynHEzAtip+edDaUgU4zHOEq/tsDzRLz6XuQCrn1X0tidFCqVQdX1USaoH6+Je3WtjPJcVNMcueUaRVync4Af1CN78ftSKQ12zjYYDAYDACtWrODmm2+mpaWFtrY2Lr/8cu666y6effZZnn32We666y4uv/xyZs+eTWtrK7feeiuPPfYY3/72t9lvv/0olUqcf/75vPjii9vd14haQNlTMKuplMJ13Unfz67ITLLWNxgMhtGgSz2ocq888KUlEk2z4z60Owtai9B1qyKQqyWwHJTtiMmXk0qkUfvQtRH6ugZPGDS0gq/x7/u11BsfdTIqWxAzL4iMvij3QqFJ9hFGV3cFx2W3KmMbksoGqdVldH933EIKDZlCPDbF1nid4H0CpH7ZD9KoG2fFtca1cv1+kq5d+aYJ7RVtMEx3ZtJ1anisa087gsbU1GXWdNdc5v/24RkxRpPJ8uXLOfzww9l999254447mDVr1pDLbdmyhZNPPplXXnmFRx99lMWLF1OpVHjta1/LX//6V/7pn/6Ja665Zpv7GlEkOUrnGmOIe6Q3g8FgMBhGgtYa3b1ZosghKmjh44zeoGPaoxTkGtCV/lhc+YEDcrW/vrey1ui1L0kkObm+7eA//Ef8n34D1q2EjavRv/w+ursrcmdWgHLSkG9CWaldrw7OHtAfOnKtDuqTqyWZePD9eALCGeBonXgfsMI+10E9eLSfZERe1T+/q425wWAwjJD//d//pbu7m+9+97vDCmSAWbNm8Z3vfIetW7fyv//7vwBkMhk+97nPobXmj3/843b3NeIpkJGI2GQvxYlc1mAwGAyGOtwqVPqkHRIiRJSTlkip2rkqibTvQfcmqbe2bPCC+uSwljWkUkJXK/j3/BS6t6BefSI6kwvcl22wU6hZc9ArnpbllQXpFGxZC4v2kRRhz4VKn6yjPVlPFt7pxnVIlCV1wb4XCNzEOaezkoYOImTzTYPfA4ids5WS9yq8zkmmqlu2rO97Ir7DdOudPVJvMOwsRG74U7g/A3fccQfFYpHDDjtsu8sefvjhFItF/vCHP0TPHXvssaTTaVatWrXd9Uf0i+f7/nZvX/3qV0mlUmitec1rXsO3v/1tnnzySTo7O6nVanR2dvLUU0/xne98h+OOOw6tNel0mmuuuQbf9/G8nbCGzGAwGAyTQygmfE9uThoaZu2cQq63A8p9cSpwOi8Rx2QbI88Vh+vNq1GNLVAuoR+5G6oVtO2gLRvtueiN6+GQ4+DAo1AnvxOW7Au5orxW7kOHUVAIRJsjt0xh17lICzMSBnyWVCYPTYFhV/M8lFeT92PgZH+uQWqO7bREmdM5Sdl2q/X9mT1XJj7CjADb2XXG2GAwGMbApk2bRqUZfd9n48aN0WPbtikWiyMq8Z2QZPr/+Z//4YorriCTyXDDDTfw7ne/e9AyjY2NHHDAARxwwAFcfPHF/OQnP+G9730vH/nIR9i6dSuXX375RByKwWAwGHYBlO2gG9ug1CsCZGeu4xwowrRGaV+e9j1QBalxzeRQre3QKv169fN/Q29dDx02qubiP3wPav5i2GMfVEMzoLD2PUwmFzo3RAJO5xokkmzZIuBAovb2TjzGI0SFtd+VfnAr8qRblTEEdKWE7twIuQIqlUXZtkxwaA9qSLS+2Cr/V/qC9Su7Rr23wbAToaypnZPdGed/x8Ls2bNZu3Ytf/7znznhhBO2ueyf//xn+vv7WbBgQfRcrVajo6Oj7rnhGPeQP/bYY1x55ZUAfOc73xlSIA/FOeecw3e/+1201nzqU5/ikUceGe+hGAwGg2EXQmUKqOZ2VKF5566dLTZLpDwUrn0dUY9jAKzgnrLicWiZA4v3llTqzavwlz0Ma1aKU3XnRlRyWa9WH+H0PYmG1o1p2P9oF0RrqUGulqFrk0T2q/3x5EVQ+60r/fgP/xa97AH0i3+Dvk65ebVByw4yVBvY4stgMBgMg3jjG9+I1pqLL76YFStWDLvcihUruPjii1FKccopp0TPP//882itWbx48Xb3Ne5I8re+9S1832fp0qW85z3vGdW6//AP/8AnP/lJli9fzne+8x0OP/zw8R6OwWAwGAw7FcpOwayFUg/ruehUFrwqoEQ8ZwoS2fTcKGVXZ7Ji5hVuo30e6jVvQmsXtq5H9/egGlohkxcxnM6L8AOpxe3rlP9DBvZf3lXQWszhPDcYp5xEjpMiN1OQRTs3RX26VUPL0NvLyrLYaVBl2b6yYtdrg8FgMAzLFVdcwa233sry5cs58MADede73sWJJ57I/PnzUUqxdu1a/vSnP3HTTTdRKpVoaGioy1b+yU9+AsBJJ5203X2N+1v5z3/+M0opjj766DGtf/TRR/Piiy9yzz33jPdQDAaDwWDY6dC1CvqVZVhzdpMnbAfKvWgng2qcLeKtZS64Lrp7K95Pvgab1qCOPDY2Tp67RERwaJq59kXUvkeL4RmgLQta5klbqFAs+74IQGcXFcgg4tirxb2TtZYJA62lpRMqEriqcRY6rBPv60K1zk1sSEmrrnTQmsyyINckEfxw2waDYeZgjLt2CIsWLeL3v/89Z511FuvXr+e6667juuuuG7Sc1pr29nZuvfXWuqjxgQceyJe+9CVOP/307e5r3CJ5zZo1AKTTY2u5Ea63du3a8R6KwWAwGAw7HXrNC9C1GT17ISDdIXSxRaLIbhXtBgZS1TLeTd+Elc9LG6h770K99VzU4gNE7HVtQlX6YfYiEWvZIkpLRFRZFrpakh6/qQxaKZTWYNsSHa2Wgqh1fuZcrPmepEiHRlyhcZayJHKejJR7LlRKIogJorvpbH07Ma2DC2MLGtvAraBrFahWIJ1BAdb+x6BLvZBvQmfz8SSE9qHcIzX0uSIqnZVt+RrKoWN2IJad9M7ZxsxgMBgmgGOOOYZly5ZxzTXXcOutt/L0009HZl62bXPAAQfwjne8gw9/+MM0NzfXrfuud71rxPsZt0hOpaSlwVNPPTWm9f/+97/XbcdgMBgMBsMAqiX0K8tQTW3oWfNRnot2q+JGHYhYrTVqn4MBhV7xrGi9fY9ChYZQxWb0iidRxWZU+x6Ssu1JqyJdLUNfB6TSkMnFPX6rZRF2liXmYJYlKcczgVJPXGtdIxb32pUoeb4xeKwlld0dUJvtVqHYIinS1XKcFp3OokO3cZAx8z00oFJplN0Mze1x1L6vU9b3XBm/rk3o1vko34NSVyDWFahg355rUrANhmmOshTKmroJw6nc10ygqamJ//qv/+K//uu/qNVqbN26FYDW1tYJ05Tjzp9aunQpWmseeeQRHnrooVGt+9BDD/HQQw+hlGLPPfcc76EYDAaDwbBz4PsilrRGLdhLIpfVCmSK4PuRcZcCcIPlUhnsw0/C/ocPo455A9aFHxc3ZiT6rGxHapvbdxdxVukDt4xWCnq3QCqNCoRjaOqlK/2QzogQTGWCSOsMQQ9hhhUaaOlE/2Kt49tAPFcELlocqi1bBGwwDiqIFCulAkfwQNwmt+W5sn46i0plIJOVyHzP5nh7A6PzQx27wWAwGAaRSqVob2+nvb19QoOu4xbJZ555JiC532effTYvvPDCiNZ78cUXOfvss6PHb3/728d7KAaDwWAwzHxqFRGtfR1Q7kWlMlj7HY2156tQji0CKzCOknTfPtC+CLB0Bp3KYL3ubViz5wQCT9C+hzVrLqrSS+RUrTX0bJHtpTJ1h6F9X6LHSQFnzaDo5rAR7+B8yr0SSS/3yGM70YLJsiXl2bKoc/Wu9suY1EQkax26fiu05Ui9sm3Xj5lbq4sKK2XFxl9RdDuxD8uW7RgMBoNhhzHuX7tLLrmEr33ta6xfv55XXnmFQw45hI985COcd9557L///oOWX7ZsGT/84Q/56le/SqlUAmDu3Llccskl4z0Ug8FgMBhmPtVSfL9WhmxRhHMyilvuQ/tuHAFVKhJ5CsBxoFZFaY0u94mYKzTG6ytLeiBrX6LEbloEtZMO2hFpWTdTkKh1aFIzQEhPa9I5Od66ut8o/i5CtVYJUstt0CpufhqKV1/Hhl0htYpEg/u6pPY7W0DZVtySa6DJWSoj7uOBaZdWFiqTk0i+H0anFaRy8l4wxYZABoNh9BjjrmnBxo0bWb16NX19we/cMGyvp/JQjFskNzY2ctNNN3HaaafR19dHqVTi6quv5uqrr6ahoYHddtuNfD5Pf38/q1evprtbfqjCEykUCtx00000NjZuazcGg8FgMAyJ1hp6t4rIyzeicg07+pDGh+3EgjgSXPGPvwJ8t4YKn1NKamJ9L+6VHPTd1SDiOXgt2o5lB67NYtClc0XZhufG/ZMD8aaUktWc9MxzuVYW4A7/umWBl1jWceJexiCDncrF7bEgiDJn4zplJ1UvpAdezOYbpZe11uhUDpXOyIZzDSK4lSXb9F3wHVOLbDAYDNvha1/7Gl/96ldZvnz5dpdVSuG62/gdGIYJ+SY+7rjjuPvuuznvvPN49tlnIwHc3d3NM888Ey03UOHvs88+/PCHPzT9kQ0Gg8EwNrSG7k1xT+BqPzqVQc1kd+BMQYST9iUaGrozW1YcNd74Crp1LipMEVaORDbTWXS2GNTHBuLYrYkg7tmMbpkv94M0bAWRA7OIMx0+GxHWP+NWRRDOtLHdVgTGsmW8vZqcGwq83uQCdQJZo6DcB16XsWiD0QABAABJREFUjFsmHUxOKHCyaK8GpX7QSurBvRrKrRJNTngVUDnJEAi36yUEttcnotpEjQyG6Y2l5DaV+zMAcM4553DLLbdsM3KcZKTLDWTCpoQPO+wwnnjiCb797W9z5JFHYgWtFbTW0Q3AsiyOPPJIvvOd7/DEE08YgWwwGAyGsdPXWZ+eDFEUdcYSpjWnMnHk1k6hUzl0rYLfsQFdK6PXr0BXSuBkpFbY90TAWU4gtEUoq0w+SDv2RTD3d0emX/LLPNTFl4qizAriMR3jxcYOxbKDlk82KLv+NaVEHGfyUgds2fG4O5m6NlEaxDG7e1MQAVZxKjpBm6e1L8LWNbD2eXT3JkmzDgzYAEnfhm18Rmfg+BoMBsMU8ZOf/ISbb76ZxsZGbr31Vvr6+gAp3XVdl9WrV/P973+fpUuX0tbWxl133YU/xmuCCc3pSaVSvO997+N973sfvb29PPnkk2zatIne3l6KxSKzZ8/m4IMPplgsTuRuDQaDwbCLERomKa8m4sYL6muzDTOrbnYoahWpVwWw05Ar4pf78b58KdYJr0dlsiLMnBSqoQWVbwKQPsdKgZNB922JXJe11oFwdlBuBZw02vdRVlB/29AqEw3hPsMaWZQ4Ooep2rYTRFtnIOGkg/alpZX2Y1Fch5aJBHQQXU4Hn6+aTBbkiiKGK/0yHqFRl52S9y0pct3q4Kh7tiD/p7NQKwVtoWyiKYuwf7LBYJjWRI72U7g/A/zgBz9AKcWnP/3pQabPlmUxf/58LrjgAs466yxOPPFE3va2t/Hoo4+ydOnSUe9r0gpfisUir3nNayZr8waDwWDYRdFuDbauBs9Fp/Moy0Knc5AtRi2MZjRuJb7vVfFeWUHvJRegOzvINcwidfTR8lquIBHSkHQeLAu98kms2Qvi55VCV0uoTCF6LFFQHxpnS7Q0W5Toc60i4g0ALc+HQm9nuEhTltQCw9Dn43nEQldLnXC2KBkLYSp6piAi2UnLtpx0EP2vRYJaItLZeD/KljTqsN5YWVCcFafPD1fPbDAYDIaIxx9/HID3vOc9dc8PjBYXi0W+9rWvceyxx3LVVVfx3e9+d9T7Mu4QBsNMxPelbYnvS3SkVo4v/gZFRgxTgXar0kon6JWqNHLBXGiaeWZH053+zthcqdqPbpkvEWXPFfGSyW9z9WmPJeZRGsDz8B65B93VCUDpll9gFTLYJ7wJlUqDW0XbjqRGW7aI4YaWRA2Wku1tWQttC9DZIJMrlYFCc310Ikw9riXS121HRLtble2ENdIzGaWCyYByXJMcntPAcwu/TwPxq8P77bujkpFo7aNqVfTc3UXwZvIolIhsFbwH1hDfA8Pt12AwGAyD6OzspKGhgebm5ui5VCoVpV0nOeaYY8jn89x5551j2pcRyQbDTKTcKxetWte3hSn1QKF5hx3WroqulaFzoxhr2ClUZMJTldTOnSG6OZ0YOBGkfXStIoKv3BukyM7gnzffEzGmNVgWqdccT87TlL55DWiNdcQpqAX7QKVX0rGdtHzmwnZOqTT0bEFXy6j5+0TfEXrLWhHGze2oQvPQwsyyJWU9TGPXOu617AdCOTXDjLtCtC/n4nvx92aYSj7cxEr4t5zJg1sN2mulB49dtQy+G1d3hynrtpm0NBh2Soxx1w5h1qxZUQvhkObmZjZv3kxnZ2edeA5Zv379mPY14VcRDz/8MD/60Y/4y1/+wqpVq+jo6MD3/UHW252dndx///0ALFy4kIMPPniiD8Vg2IlJ1L0ljXQ8VyIktmMiylOE9lzYuk4eWKl6C6RQZBgmlkJz0OO2KuKl3FcnWnStgpqhIll7ngguy4rriYH0696IamrGWrI39tJ95e873yifr0q/uHunMpJ6rjU4GVRTu0SbU2lYuA+6c6PULzfN3nbkMuwV7LsMNvWawZ/ncl/9pGJkXZY8p4Hnl0iD3mat+wweF4PBYJghLFiwgMceeyzyuwLYb7/9uPfee7n77rs588wzo2Ufe+wx+vv7aWlpGdO+JuwqYtOmTVx00UX87ne/i54Lf9yHKjYvFAq8733vY/369ey+++68+OKLE3UoBsPOT6YgIsH3gwvZAN+XaDKIkJihQmFG4dWPv7bDtjpW0FvWvAcTjVIWNMxCd22SNjrdWyFbQGdy0NMBLU5skDSD0FrjP3UP1MpYux+ETge9eG3pnZs6+nj5Pe3rhHyTCOVKv6QNh+7VSsn4zFpQN1GmmttRze3bPwjfg1J3/XOhc7blzLz2T0l8r/5xaE6WysXPWU6irji4PxJSWfD769t2GQyGnRg1xX/n5jsF4NWvfjWPPfYYDz/8MK997WsBePOb38yf//xn/v3f/52FCxdyyCGH8MQTT3DRRRehlOLYY48d074mpFBu7dq1HHHEEfzud78b1PJpOFKpFB/84AfRWrNixQoefPDBiTgUg2HXwHag2AoNs+qf9z25YK6VoVoZel3DxJLKxMJBa2nxooKLa8ua+U7L0xTt1sSNudIPmSx6yxr0K8+gy30zt+SgFtS1l/vwnnkA99E/Ql+XtHAa6KRaq0LvVjn/EKWkDCPfOPZMkrpIa4DWMqa54swWf9aACSvLDiYbEpdCSkl2Qr5JJlpGer6WLZ4Q+aaZPZFgMBgM05g3v/nNaK255ZZbouc+9KEPsWDBAlasWMHRRx9NNpvlqKOO4umnn8ZxHD7xiU+MaV8TEuJ4xzvewSuvvALA/vvvz+WXX87rXvc6PvnJT/KNb3xj2PXOOeccrrjiCgD+8Ic/cHTo2GkwGEZO0hlVJ9z9/CEudg0TjlIK3To/ijwpBbiuXHiHLXYME0/YmihAzV8qtbTZvERSJxq3GmVp6PCx5UDjrMH7c6vSUklZkvVhWfK3We4VQ65aBd29VZbL5qG/B2plSbWu1SCVQilF5e9P4ZZ8Mnv0yLbm7RkLZUvFUeZwvttOycTZeITskBPcWtK5LVvcn7UvrYrSuSGWHbCtWlmMv5K9m4dbttQt7ZeiOt5AsCZFp+fKOGo/ipzLd6Av0dxaJUgXV3Gbp2wg7pNZN0rFz08loWGYsuW9N98PBoPBMGJOO+007r77bvL52EeiWCzyxz/+kQsvvJAHHnggen7RokV8/etf56ijjhrTvsYtkm+77TYefPBBlFIcd9xx/O53v4sOfHs9vfbaay8WLFjA2rVr+etf/zreQzEYdj2UkgtI3w2imImLQBPNmDJU+D6EzFRjo5mEPWCMMwVUrjix+9BaBJnvx+ZXBBMjdkoi2aW0RA+T64T9hrUv5lph7WuQmq+cNBQa0cqSXsVOGr11HaqxGd08B557DL15LVahicxBh6OKDbFTuueKMA5/Xz1Xji+bh1zj+EWfnRIRF6KsWDiH+0LHLZC2FbH23WBbSsavVhFxPRDtQ19X0Fc4UcevCPoRp+LzCgVy1DrJJ5okqPbH0WIdtGtyq3JLZYJzCSYSo17QU4jvy+QJgA78I7Y30WAwGKYlpk/yjsFxHE488cRBz++1117cd999rF69mlWrVtHU1MR+++03rnEbt0i++eabZUOOw3XXXVen7EfCwQcfzJo1a3juuefGeyiGmYT25cJF2XJRZBg7+UaJKIX1ddWSiDRz8WXYiVG2g25dINHHVCbufTuRlHqidGYNQSufhCi0LKhV0aUeSOeGNgvzXLTWkUVU9HPtpLBSGSlNSmWg3Is1a748PupN+M89TnbpwahiS/13ZHIfjW0iyJPR0vFiBxHfWlUOVlnyXT0WfD8YOB1HckNjwTALwE5JJH2k+0hmy8RPyn+Ww5B1e74LZGSMwvZWKfP9aDAYDDsbCxcuZOHChROyrXGL5DCKfMwxx7BkyZJRrz9nzhxAjL8MuwhaQ393fLGTKZi6zfFg2fUmRRlz8WfYNVCZ/OT2RE6mcxM4mQeTUcpz0emcTFBV+0FZ6NYFMmsdpSxLjXo0k+17Ej1W0ipM6/g1nSnUPVb7H4naXuuwVGZyvjuddH2dfVjrbNlAIt3asuV17ccpznUknKPDdnUD3aXtVL2hlufWTwoM3GY4vuH/iT7F9csG97WSyViQSY3MDjR0syyZvAz7M5vfPYNh5mJaQO0Q3vve99Lc3Mz//d//jWj5j3/842zZsoVrr7121PsadzHMxo0bAdh7773HtH42K6lXlYoxGdpl0H59NGAooxiDwWDY0SQnnywLlc6IEAu/s5RVL9LcKqBjEZeIaurAHToSzDoWkNpzUflGWVprdOcmVE/HJJ/cCFFKhF2+SSKxhSYotshzvicTnqUeKPcMrmfWI7io82oDJvkKQSp9MH6JcQIgnZgUUSqo6d3GfpzU9Cp/SGXisTT1yAaDwTAqfvCDH/CTn/xkxMvfcsst/OAHPxjTvsYdSbYCV0jfHyoFavts3boVYMjmz4adlPDCMowemNpZg8EwHXHSktLcvbm+D7OdQoXCLPQBUEE7pkp/vVuyk0J3bwoMvAb85PZ2orUvtcSB2NbaR3dvQe15yCSe2BioVYLWUCpwcE4lJgV8cD3o7RDTrbAt2vYEbIhlo520/Cb0dUAmnzAoG5BCrZQYnKGlnlsTjLeNjt+VWFzr+skKg8FgmAhMTfLMYHvdlrbFuKcxZ8+eDcDKlSvHtP5jjz0GwPz588d7KIaZglJyUZgtyv9GJBsMhumI9iU1NpNFKxX92CqtA0HXJS2/nBSkM6gwSyYyngoinfkmaeFkhYJNSc2v79UZ7ukgAq3aF0+/Vkulnji1uhz0Ytc6vskT9X3Dw1rk8FwGiWYNykZX+uJ06UAgawhKSeprrXVQqiNp7bIN7blo35fU96Spl7KkLrpmMtUMBoNhV2Tz5s2j9ssKGbdIPvzww9Fa8+CDD9Ld3T2qdR966CGWL18+rkbPhhlK6AY8lNGNwWAw7EhqVSj1yq1WjoWfWwPfR/u+CFrtQSqLSueTMcygNZEdZMy4KNtBpTL4PV1xuYmTilOL3Sp4tSBSoFDZfF2G8bRgqHrfsCZ5pOvrwBk7es4SV263FiyiopTzaPsDJwu8WiSANYmJBQUqlYGBUWMVWqYZDAaDYVehq6uLL37xi/T397PnnnuOaRvjFslvectbACiVSnzmM58Z8Xq1Wo1/+Zd/iR6/7W1vG++hGAwGg8EwPtwq9G4V1+hkKySNTOxFgrkaOFonSkciv4XApMod4LewZTX+8ifQYYQ1nYVcUVKKfY1WSlKIAeVMswnEfKOYbNmp2ElcqXpNajnSq7jOxEsnbgPQPlRK0eRDFGUniAIP5dBfLctERWByppJR6jBqnRTWUd2ywWAwTCChcddU3nZBPvnJT2LbdnQD2LBhQ91zQ91aW1v5+Mc/jlKKs846a0z7HvcvxznnnMMee+wBwBe+8AW++tWvbnedTZs28Za3vIW//vWvKKU47LDDeMMb3jDeQzHMALTW46oPMBgMhkmlVpG6WktEV/htpZRC2XYQ5dWobAGVbCOkFNqroWvlSJQlv+t0Twe6Yz166zrwvDhqatmoXIMI5tDbw04Nrl/e0dgpMewqtsQZQOHkQChMM3kx4Sq2gJNJiNVtXNz5riwfRYjlpkKxPZDGNih1xfv2/TiKP5RAtmxT0mMwGAwzmFA7hJOjycfbuqVSKd773vdy6aWXjmm/4/4VdhyH733ve5xyyim4rsu//uu/8sMf/pBzzjmHl156KVrul7/8JevWreO+++7jZz/7GaWS9CrM5/NjsuU2zDx0uQ8614mXSnO7XBgaDAbDtEKBl2hJ5NYgm6sXX+lM0CYpE5hZyY+4/8xfAbBfdWK0qO7agv/oXdDcKk+U+tEbV6EWSPpX3DIqiC6nciKYZwJOWvqygwjaZPmMkwavSp1AdjISHe7vDJ4I2l2ls+jGOehKb7z0MKU4KpVBN84OHqigDVZOJjUq/SK6Q0duOzX9arsNBsPOwcBJuanY3y7IhRdeyEknnQTI7+zrXvc6Wltb+elPfzrsOpZl0djYyN57700uN/a2qBMyVX3SSSdxww03cOGFF1IqlXjsscciQ64wFerMM8+Mlg9n14vFIjfeeCMHHXTQRByGYbrTszmuNeveFKfsGQwGw7RhQKaL78rXlkIiyGEEuFYRIVZowV/1PPreX4BtoV59ojhWI6nAfrkP1q+GfCNqr1dJ5LOlHW2nxA06WwR0LA4r/TNHJFs25Jslqms79RdxTgooSto6AIEPRVB3rbUWge1Vg8jxgAsZ3wt6UJdlvVxRxrzSJ2MV7surBSneQSTb9+IIssFgMBhmNIsXL2bx4sXR40WLFtHe3s6JJ564jbUmhgkr1HnnO9/JQw89xGtf+9pB4W5g0OOTTjqJBx54gDe/+c0TdQiG6U7yomW6pRIaDAYDSPSRRKq09qEcmHhZtqREh79rgL9hJfr3P4RyH+rA12DNXYLyPJTvi0DeuEoi0WuWAwo1d3dUQysq7Dmsw1rcgJlWP2tZIoiHinI4Kcg2iIjNFuPosGWDW4FaSURtzxbpI53E92TcfReq/VK7HAluBqdVh//bjhHIBoPBsJOycuVK/vrXv07JviZUqRxwwAHcddddPPnkk/z2t7/lgQceYO3atXR1dVEoFGhvb+eoo47i9NNP54gjjpjIXRtmAs3t0m8UoGHWjj0Wg8FgGIDWGvo6I3Mu7buBiLUhW0BlGyT66XvoVBa9bjl63UuARh3+etTifWRDtqRs6/WvwEvPyHOz50JDKypbkMfVMipsTaR9EZIgjtc7E7YttySZQpymHaKQCLrrBlHoAQzlZWHZMm5GFBsMhilGWWFbv6nbn2FqmZRw3sEHH8zBBx88GZs2zGCUnYKWeTv6MAwGg2FI9JrnoWsjVvsStOcG9a0WZHKosGY4kwffx3/ladi8GjI5WLQU1TJHNqKUtA5+4gH0uhdRvgdNLbJubye6eY6kDbvVejurVEYybMIa5ZlQf6YDo7HRRL9DsVtolcmIWkVKb5y0hOYz6fjcU1lxGHfSgYCugHiDQyqHctJxn+bkeIXPWRMUlU/uYya8LwaDwbALsHbtWp566im2bt1KrVbb5rLnn3/+qLdvcl4NBoPBYAD05jVQ7kO3VlGpNNpJQSojbpq+L4K3vxvQ2G0L0M2z8de8iFq4B9otR6JXP34v/s++D6ecidprf1S2gK6WYdY89AuPSA3unEWEpc4oS+qTUxkRdpYt0dbpLMiqZXGZBhG56fz219EaKr3guXLemby4Vfs+dG4IosKW3LINko5Ok6zruUELlGASob8TbdkoZclz+UZJtfZq0B8cV5jmPR60jtO+lYJs48SJb4PBMHMxxl07jKeeeop//ud/5t577x3R8kqpHSOSP/WpTwFw5JFH8qY3vWnU6995553cf//9APz3f//3eA/HYDAYDIYxoVra0aufw1/xFLTMwSo0grLQSkmKdTqIKIc4aVQmh/Z9WP08etVzsnxvFzTPQi1cEqVXq3QW+rvRPVtRux8YtbGQOmctKcnhRZDviSizh0g7ni5UeuP75b6RiWTfi1PZAYVG93ZCtSTjE4pP30e7NfTqZ9EbVkKhGTV3MSqVids5eZ5kJ4Vbq5XBLtancdfK459sCN8LEMHsVobu32wwGAyGSee5557j+OOPp6enB6016XSa2bNn4zgTH/cd9xavvPJKlFJccsklYxLJf/jDH/jiF7+IUsqIZIPBYDDsMNSi/VHFZunFqD1JBa5VpEY2U5AoZYBGZqd1sRXVvUncqvulHZRqbENd/HERaeGyYZRYWVCryvJh7Nm24z7BUVrvNI9WWnbgJG1JmrhbG7qWOEninBSBj3ilH92zFWbNl9EIz7+/C736OVnWkbFSqQyyiKRShz0zo+MBUIn65ImIvAzchql/NhgMINbHU1knPM1/EqaKK6+8ku7ububPn8+3vvUtTj31VOyBvhcThEm3NhgMBoMhJJ3FIujEkMlDz1ZUJi9Ct9wnqcXKAjT+ulegFkRUW+eISMwVsWbvhrZsyObRfZ3SvihIBVaL9kP3dqEyeZRtBYIY6SGskeixk57+YizXBOWeuMa40gdWw7aP27Ik/bnSF7uHK4VeuxzVtlAe2ylIpaHWue3926lAlAeO1pHxWT5Q4L70Tx6vULZsOWa3Kvenc3TfYDAYdnLuvvtulFJcf/31vO51r5vUfe1wkRz+UCqTa7/rorVEJCx7Ymb+DQaDYSxsWSP1p01t0W+SDsSXiv610OkstW99DpWysI5/LcqyUJYN8/dAaR9qJdFp2SLYKZSd+Km1Hazd9kFXy2i/FtcxpyVKqpQdt0qabvgeENThaV/EfOjQDbETePidHlZdax33UbZTQbTXlzHK5CSC3zgL7cXp2BSaUAv2Qm9chXZrkppdq0iNuJ1CZzKARisLFaaqay2p0U6aKEo/ESZodsqIY4PBYJgGdHV1kclkOOmkkyZ9Xzv8l7izsxOAfH4E9UyGnQ/tS4qi9iU6k2+c/mmGBoNh56S/C6pldKFRnJMtRyKc3ZsDUZhCexVUXxn70KOo3fBNrEMPh8YmqZttbIPO9dHmlNYwb09Ju+7rlCdtW/YTijfLkrTjTS8DoJ00Kl2Aprbp9V1YLcktdHoOBXPUI1nHqc6lnqBGOJVIIUeiz5YlQjeoHVaFZlSuEd29WUSzW0UpGROraTbMWQzFlvg4Kv1Q7kVVSyLBLVv+zzdKL+tASMe9ky2J5E+nsTQYDDMepdSUBvhMMFGYN28emzZtwpoCA8Ud+qtRq9X405/+BMCCBQt25KEYdhRuLW4jon15bDAYDDuCYqt8D21eg+7vQTsZ9Kpl6K3r0MnorlLYBxxC6sIPU/vONXhPPYOasxh6t8TLWDbMWoCqBf2Qcw3QNCdwYyaoT1boShn6uqXmuaENCi3yWrXCtKJaju/7nvxvWbGxVfi/1iJUo/EKI7lKtlHpl9RspSSN2Umjy71R2rayU0FWUTBO1ZK4X4dbq/TJHRVE78PnezskJTqVSVqrmd8Vg8Fg2Il4y1veQn9/P48//vik72tUkeTrr79+2NeeffbZbb6epFarsWbNGn7xi1/w0ksvoZTiqKOOGs2hGGY6bi0wtTGmKAaDYZrQMleijr4H+SbYsAJqFXR/D6qhBZ3JS7RTa9A+atES0ld8CdXSJtFmP8iISWWgeQ7KrUbpyMpz8Ts3QrYAlhUbV6VSsZO1ZUu0wElNvzZDlh27PIePIxLmWTrh1K0JTjKQrdqPzc98T1yxswUZo3DdJKGBWTKC4rlS7x3vNYhA1yR1OxpYHS9hosgGg2GisdQUG3eZSDLAJz7xCX7yk5/w0Y9+lNtvv51MJjNp+xqVSL7wwguHDPdrrbnrrru46667xnQQSik+8IEPjGldwwygVpGLq1Q2vhAq98Sv28EFoZ2evrV4BoNhp0cpJRFftwbVEtp20NUKumcrulrCWrw/vpOWyHCtIm2L8lmUV0On0lCxAB8aWqSnMgTR1iASGkZY01lpKwWi5Zy0REaTv68jnTDUWiKoaDH/mqyUvFxRIsFKybGVeiEZs43cpcOa5cRrvicO2AOPTfsSWdY+uF7c3ongvbAcSbVOruf7wZjFwlcrO8pIGqizjc+FwWAw7DyUy2W+//3vc9555/HqV7+af//3f+fII4+koaFhm+stWrRo1PsatSLRg36Btv389sjlcvzf//0fxxxzzJjWN0xzamWpTwOolNDZgmTfJZdRlqQaGgwGw47Gq0GpS+5nxdWa3h6spYdCrgFLKbTviViz4p9QpRQ6nQXPq49cWg5oF+3VsJraoig0WurZtC1GXUoptOdKuyMnM/IJw2opEMmA64qYnQyUJc7R4bnnG+W7PfSTAFGoVpBGXe4DHY6THYtnJx0cbzhJ4EPzXNi6RsbVSaE0cv6FpsGTBU2zoXMD+ArtZKR2XAWTE8FxqGQrLdnJ5IyJwWAwGKaU3XffPbrf2dnJxRdfvN11lFK4rrvd5QYyKpF8wQUXDHruuuuuQynFPvvsM6KUaaUU2WyWWbNmceCBB3LqqafS2Ng4msMwzCS8+EOpqyXo65D7xVa5kFEK0tkddXQGg8FQTyg4CWTcyy/AimfgNW+OI722I5HUfCMKEb1aAz1b5fVsAdIZ2ULou2Cn4r6+KnbyV4FbtNZasm2yo5wwTHzH1qVDTwZeLeoFLa2RChIJDgnFclhX7CYiyEqBm6yz1miUnHOlP6hrdmVsbQfQklodCt9wtf5ucSAHaG4HrxqMYbDvyLArkQJunKkNBsNEE36vTeX+DGMKyo41kDsqkfz9739/0HPXXXcdAG94wxv46le/OqaDMOzEOBmJdEDdxSf9nTBnD7lv/vANBsM0R7/wBGqfV8uDcm8g+LRkwbg1VK2Mbm6X2uSO9bDbfiKag9pipX20NcRPbhjxVAqVGkNtVSodm2ol0pUnhVriO9z36gU6SOQ49ANNZeQ7X/ugAuGb+A3QAH2d6E2vQCYfa9sggq61hq5NIsydNNp2pL1WuI2wfns40vm47ZTBYDAYdgpWrFgxZfsadwHookWLUEoxa9asiTgew86GkxLHWN+TNMSwFjmVMxcvBoNh2qHtFOggOur7qN32gt33RVdLYrzluSjfFfFr2SJQqyWJZFq2pBorBaVuST1Wliyrtbgxex7gA1adMZVS9tiMC1NZiZRqPfnGh7YDkVF00PM4OfmpEvt30lBolnRr2xGxOyBK7/f3oKLIucz0a9+TcfLd2OQrFNu2g3LS6GoZfA+t/dgtXAcuYb6GXKP89sxUfF8+P74nkzAm28pgmH6YSPIOYfHixVO2r3GL5JUrV07AYRh2aqzg4q95DvQHP/Y5k2JvMBimF7pawb/zJvCrqLZ50NACLW3QswWVzkhkuKEVckV0pgjrV6BrVdSs+RIBVTa61IuaNTdIrbYCB9Rkza4FBKZUtiNmXhrQrkSnU2MQRFPVFSDsP+y5ct+yQRVE+A/lyG0lhH8Y5fZcQKPL/VDuQ2dyQFBHnMpAuiAK2klDpRRvK7hA1ACNbfI41xgL7zCdLjWKeu7pSqUvckWnv0uyBYxDt8FgMEwpM/yXxDCTUMqSyIJhx6Glzi+KgoWEZjxjTU/UfrDdMUbDxkq0X2dyW+b4fpxaalkyTlrHfWFRk5faGb5nSm3/4l9rib6F74HvBccV9LMN62PDtjq+HzsRay0Rqx3Zesj36o/Zc+WYU+n4nKLxYGjH5GHQtQr0doBSuBs2UX1lDdljXoNV60NXy2jXp/az67BLW7Da29Fb1qEOOWHQdpSTQhWa8V96Ajo2yLbLfdiL9oNKP/qlJ1Gz5okRl9YDm9yFW5F6Xk19JHYm+Es56fq0bjs18prf5LrpvKSol/tQ6ZyMR0NrFBmOOjmV+2TiQCnwPVQ2Xz+RMNMF8VAMrJ9LdLMyjAOtgx7felTfHQaDYfqyadMmXn75Zfr7+znhhMG/2eNhQn5dnn32WarVKqlUiv3222/E6y1btoxarUY2m2XvvfeeiEMxGAxDEV4cVEtRqxTSucBptgbVIL3UsiW9bzQXD2G/0/AKP1ucGqHse7GBz2Tud+B+QMbOq9XXZCorTrWdKLSWqFLYTih8z4ZbNnQbriMQZP3dgI7TciNxHCzvuZLi2dy+Yy4e3ZrsPzL0K0DPFjlmpaBxtvwfmjyBiKV0brub1rUK+rkHg8kNsC2b6qN/ofsH32X2B89BpdLg+9i6BP196N5eyOehUkZli/J3Uy2Ju3O+Uf5G+rriHVT6IVtE90mKrF75d9htXxlnJx23QArHO50NJiqQyGfYJzg1yTXF0wilFLTttk3tp3IN0pJrVyMrde74XtRX2zABuFXpuAEysZPJ79jjMcxwpjjd2syU1fHLX/6SK6+8kieeeAIY7GDd0dHBueeeC8BNN91EU1PTqPcxbpH88ssvc+CBB6K15vzzzx/S3Gs4rr76aq6//nocx2HlypXMmzdvvIdjMBgGkhTBSTxXLuC9Wvyc7w1onbIdPFdE3MDnJlske67UtydTECdrvwPNiWCwQIagrc8oxm5E6Fggh8cyrEj2hxDIwTbCProQi+Oon22wmGWJMVNglDSl1CrxREQYRevvhHSQZur7gycqQM5rBCKZvi5xSt6yETo2Yx13Oi3/9BHwfXSlhP/wHailB5O66P+hS314P74GtehA7CNPkfVnzR+0SbVwH/RLwY/zov2h0ISyU+gVT6I3vILu7cI66vTt18amsmNLsTbsvFg2NBqflwknmbWR/N0zGAwzis997nN84hOf2KZrdUtLC7lcjl/+8pfceuut/OM//uOo9zPu6cmbb74Z35cLs0suuWRU637oQx9Ca43rutx0003jPRSDwTAU4cz5QMI0xTphOYqZUbc2WCAP2l4CPzBuK/WM7AJFa4lQ93eJOZLWErHr64wN4JJfkJORdul79RdW0fNDfDGHKcwTiqqfCNjWOW5r/8n1kkLe9xNPa3SYcj+F6P5udKl78I+d9oGEoK97PeyxO4L8ZN+Tkt+N6+HZx1GL95XoLcjEQCqD2m0vrIV7yWc3X8Q/5Tzs15+1zc1aC/bGOuI0rCPejDV/qRxVNo913FlYx7wV65gzpOexwWCYHiTLAoZymjcYRoNlTf3NwIMPPsgnPvEJHMfhS1/6Eps3b6a9vX3IZd/znvegteaOO+4Y077GPeJ33303APPmzePwww8f1bpHHnlkFD2+6667xnsoBoMBRPhU+kRQan+wcNJaBJVbDW71vUtHhNZDR6czheFFVrUsx6b94Ni2sy83iGpqLUK/Vk4I1vCcgvThTGFyosjJ9HSQCKtlBa1uAiwn7m870SJZKUnhTuckNXCoCK/vSZpyuSdwwh0iJVtZaDslacfaiyLhWvsijsPPBBqdjFxPMjpMsR70gmZIo6Kwdno0++jpQN/7Syj1oV5zmrhVB/XCUd/iWXPRUQ8ii/SS3Ue0bZUtSI1s8jnbQTW0iEOzwWCYPqQy0hos/D41GAwzjq985SsAXHbZZfzLv/wLra2twy574oknAvD444+PaV/jFslPP/00SikOO+ywMa1/2GGHobXm6aefHu+hGAwGgEpvLH4rfbEwDcWeZcftWMp94kw7xkbrMYGYm8wo5FBpzyjpxT0ldUGDo6+ApNOmMpPnPquUiN7hzJH6O+Oa2XKPHMuAtGtdLUHPZql5tVPo0GQsEMQqTL3OFSfgszAKtB9FdSVYrGMhPHA8w7r6yDBtqNTyIXbx+J+gawvW8WegFu+LSmeJItTVCr5Xw2oJZ6GDNkKm9stg2PlQSr6vnbQx7TKMn/B3cypvBu677z4APvzhD2932ba2NgqFAmvXrh3TvsZ9Vbdp0yYA5s6dO6b1w/U2btw43kMxGAxQL+J8PxYdWgevhbWpQfpO8os3PcL+1UrJjLyy5JbObV8kprOBYZQls/jb209SGFp2fW2uHIRsw6tKvepkREDD8wqPeaBAHo2z72RR934HYxA6QGtf0uLD9HZloZQSCWjZYowViGKlVHCbupQuZdnRfnXY53a4CwIV+B3rQChrRjQxoft7Ydbc+LyD/aEsdF8nVMuDXKinMppuMBgMBoNhZGzcuJGGhgba2tpGtHwmk6FaHaJsbgRM2NVQrTY2E4TQiSzpSGYwGMZB0sgonYPMgMepHHW1x6H4GC6ddziclGxP+5J6PVT6dRLLlmhzrmFkwlIpOSbbGSyALVtEepIhI83jxLLleHMNchxhLSvIWI1E7E822UJ8P1MM/s8HbublIGVbzkENjBI3zkZZVn2S/VRGki0bbPnMhWK5LuU/KZjDz0L0mjUi0y7r0BOD92jA++TV0M8+BL2dg1fyxvaDajAYDAaDYfIoFAr09/fjedufzO7t7aWzs3ObKdnbYtwiefbs2QCsXLlyTOuvWLECYMQzAgaDYTsko2thqm6hRXpUp7MibnONA9axx5YuXOcWOvIU2BGhNZR6B4tf2xGx7TjUCZ+pMJyyHRm7XMPIXJWngnRe2iM1zpb3F0R8Ns2Rdk4Ns1DalwhxmE2gNdipqG5WQSyOp7rvbGhGooao4Y30spKJlXwzFFvlVmjevnM0oOYsxDrq5DjFHCIzMOuY01FtCwaU4qupd/c2GAwGw8zCpFvvEPbZZx88z+PJJ5/c7rK33XYbvu9zyCGHjGlf4xbJ++yzD1prHnjgATo6Oka1bkdHBw888ABKKZYuXTreQzEYDADVhON0tST/q4EuyXYcdQRAx8uOhjq3xSGidWPF98XQSbvUKRgnHfdxVpbU0KbzEimdaKMktyYivVKKBWRoIlYpTU7keqyEKeEhWktbpUpJzsOyxagKHdf9ujX5HNRvaGInOraH78Xu69qT+vLkZyisTc41ymfNrcbnNEJ0X7fso9yNrpbR1RK62o+yLNmTk4o/T0qeM6ZbBoPBYDBMP8444wy01nz2s5/d5nKrV6/m0ksvRSnFWWdtu1vFcIxbJJ988skAVKtVPvnJT45q3SuuuCLKEw+3YzAYxssIherAWcmB9bYjwcmIs7OTHp+7c+RgXQlaPfUNFmupzOCaaWWJyLGGEIjhtkI8V/YxknpTHTiE+64YoNUCB/DQYdsP+kNPZWryCNFao/s6oFZG+y5U+9G1cuAiLWOnQc4xF0wuJCdRpvKcBn5eBopT2w4mQCx5/6olGftaedg2Ytpzce/6Fe6ff4+/eR1+x0bwfZSSunalNVipwZHryDBMDR3VNhgMBoMhxESSdwgf/vCHWbBgAT/96U85//zz+fvf/x69VqvVeOGFF/i///s/DjvsMNauXcvee+/NBRdcMKZ9jVskX3DBBeTzUht4zTXX8PnPf35E61199dV87WtfA6So+sILLxzvoRgMBgjaITlyyxSGX26QMdIYvg6UisXrWKNvvg99HSI6q/1iwjVQIKdzIzcVq5billFhnbRble3XKiMz+RqoE8PjGctEwiSifR9d6Ucno9od66C/p96MyvciUanCMQxN1FIZuR+auE3lD3FYp27ZMuGSziUepwMRP4x4H0LMa8+j9qOv497zW+xDDkelU1gts9FhWrlSYDuoTA5t2Wg7LWMT9FKOj8v0ozQYDAaDYbpRLBb51a9+RVtbGzfccAOvetWrIvPnbDbLvvvuy3/8x3+wadMm5s+fz2233UYqNTaD1XFfCcyZM4ePf/zjgTMpXHrppRx77LHceOONrF+/vm7ZDRs2cOONN3Lcccdx2WWXAXLR8rGPfYwFCxaM91AMBgMEdbMNcZRwOJSSKLA8iOtZpxLfh8710oqq3C+PvVq9GM0U6g2ztrvNhGD0vCDtuG/AMtsRu5YV16WGQhKCMQpEZCq7Q2d2te/Dppdhy2rY8BI6iHaLY3N/HGkNa7jTeaj0xT2Rw/6+TqJ9lZ0a22TJeEhlpWY+1xBPuhSag8fJEgEnrpcORXQCrTX+3Tdjp2ukTjlV0qnDWyaPjlqgOVF/ZBUaemkflZwgmMqUc4PBYDAYDCPmkEMO4YknnuCiiy4ik8lIBl3ilkqluPDCC3nkkUfYZ599xrwfpfX4c+u01px11lncdtttcZQiIJPJUCwW6e3tpVKp1K0Dklt+2223jfcQDNugu7ubpqYmurq6aGxs3P4Khl2L8CtgRwi+agm6N8ePU2kRQFqLSVPSbGmkhGnbIALQqwaR41AEKallHokYHGpsduR4JdDlXtia6P1XbEU1tuG9+DhW+26AQm9eg7X7wfE6yejyNDynERGmRA98urcT/9ffk/v5BuyT3i61xVqjfQ/d2Ym1aC9J2w4mTXQgtlWlL6hJDrabKUy9gZnBYDDsosyk69TwWLd85G00ZqauBWR3pcasr942I8ZoKqlUKjz66KOsXbsWz/OYO3cuRxxxRJTlPB4m5CpAKcWtt97Kf/7nf/KFL3wBPxGlKZfLlMvlQetYlsXHPvYxPvOZz0zEIRgMhrGyI4XRwFZQmlgEjUUgg0Qmk/2VK34gkjVgxRHLkTDUctNFSA6c3wxSrq3WdsJot2qt718/cBIz8cJEH93kMdyx5ooSha5VsGbNjc23wohxIWjX5aTAapTocViPbaeCDAQVp54bDAaDwWCY1mQyGV7zmtdMyrYn7ErAsiw+97nPsWzZMj7wgQ+wZMmSIZdbsmQJH/rQh1i2bBlXXXUV9iB3VYPBsMtgO9KmKN8ITbMlzTaTl/Tb8Qg3y45TzdM5STVO50YnkEeBrpXRPVvQpd4J3/awDFdTnk24lu9CrYz8x/6MXr4MXepH56QWP8xY0kpBsibJsuonYcL0+oEmcDsY3d+N3rwK3dc14AUtLt+lnlE5fe/yaB8q/XIzKfUGg2E8GOOunZ4Jzyfba6+9+OY3vwnAxo0b2bBhAz09PTQ0NNDe3s6cOXMmepcGg2GGoT0PNq+GdBZVaJIWP9EPwCjqj0dCWOc6SWjPha3r4sdKobLbMEybKOxUEAH16uqmVbYoIgAtEw47OVpr9N0/R99xizzR2Qm77YHu74J8kzynLEhP3mdgMtCVflj3ojzo2oResE/8uYrcypH3unH2tBL305ZyX1yr73syaWYwGAyGGcPTTz/N5Zdfzv7778///M//bHPZSy+9lOeff57PfOYz7LvvvqPe16T+qs6ZM4eDDjqI17zmNRx00EFGIBsMBgD8x+5Av/y06OL+LujtmJbtlEbEwH7JbnVq9uukRQQ6GWm/FV7wW0H/6O0Zt+0EaK3xf3cD/p9/mXwSvX4deD70d4lrtWWjZtCEgfZciRQnqZblb8T3wB3wmRtJWzND/TiZMTMYDOPBRJJ3CD/84Q/5xS9+MWzGcpL29nZ+8YtfcMMNN4xpX2bq2WAwTClRFLl1XpQOi1ebuSI5lal3wk6mO08iSilUQytq9m6o5nbULhZJ1J5H5X//A/3w3ZJKHV5ALNwT+5yPoubtiZqzO6rYiso3zZgJA92xDtY+D90b48+Vk5aShL4O6NkMtRKRy7qdMiZjIyWVHfq+wWAwGGYEd955JwCnn376dpc955xz0Fpz++23j2lfu9ZV1XZ45JFH+NSnPsUb3/hGFi5cGDlz77333lx00UX85S9/GdX2fve733HmmWdG21q4cCFnnnkmv/vd70a8Ddd1+da3vsXxxx/P7NmzyeVy7LnnnnzgAx/g6aefHu0pGgw7HGXb0DwHujYlevaO0aRrGqCUgtb5MGsBtO2GcqbO7XJXpby1g1+degY/+dINPHX/s/KZamiAQ4/F/uAnZ+x7oD1PsipAJo0KzbBwP5i/N3RvilOF0dLCq2EWFEdQv6+1pGX3bpU65pk6ITVe0lnJvsg37ZiWdwaDwWAYF6+88grFYpG5c+dud9l58+ZRLBZZtWrVmPY1IS2gdgZOOOEE7r333u0ud/755/Pd736XdHp4Qxzf93n/+9/PtddeO+wyF198Md/+9rexthH92bx5M6eddhoPP/zwkK9nMhm+9rWvcfHFF2/zmGeStb5hCtBaahq1liiossCtSO/gVGZyIm6eK2nIlgUotFdDd29BKRsaW1GprKmpHAXa99DL/wZ93aglB6KaZ8vzlT7o7ZT3sWEW9Hag1zyPapkLjbOkX7BSsHk12vdRxWZUOieRyh05SeH7QW9nLzbR8j20W4VKCX/zWtwnHsN7ZRWpJfN58Nqf89S9j0WZCOeccQiNZ56Nc9Y/xq7WQ+4n6JuNkhplZQV/DxVxt3YyYt61g9Baw9rn4j7ejW2opjno7s1SgxykjCulROiNNBrq1aC/O36cKRiRaDAYpg0z6To1agH1r2dNfQuoL/10RozRZJLNZikUCmzZsmVEy7e2ttLf3z9kp6XtMaIcrde97nXRfaUUd91115CvjYeB251q1q6VfqPz58/nne98J8cffzyLFi3C8zweeOABvvjFL7JmzRquv/56arUaP/7xj4fd1ic+8YlIIB966KF8/OMfZ88992T58uVcffXVPP7443zve99j9uzZw7bA8jyPM888MxLIb3/723nf+95Ha2srf/3rX/mf//kfNm7cyAc+8AEWLFjAqaeeOsEjYthpqZbiulmvJoIk+bjORGsC0DrqS0tQBqgAVWw2/WjHiF7+BPr5RwGF3rAS6w3nyQsbX0FaXQn+E39CzV8qNcq1ikxWlHrQHRtQ8/cUh99qf9AaaTtO2NqXbShL9lDph3QONRFiq9ybMFQCvJrsQ2twUlgts7Hnt2E7ZehYj52hLhpqn3I2zjveN3yLq2g/fbGrsfalltutyhiA3N+BqdlKKfTsJRLxtR1obItf1Frqkm0HXWhGOTPLiMxgMBgMhvHS1tbGunXr2LJlC7Nmzdrmslu2bKGrq2vMnlgjujr905/+JD/eWg+6CAlfGw9DbXeq2XffffnMZz7DWWedNagt1dFHH815553Hsccey/PPP8+NN97IBz/4QU444YRB23n++ef5whe+AMDhhx/On//8Z3K5HABHHHEEZ5xxBieeeCKPPPIIn//853nve9/L0qVLB23nuuuui9K7/+mf/omvf/3r0WtHHnkkp556Kocddhjd3d185CMfYdmyZTiOERuGEZDoYy5GQANboWiiescJYRvJKqYNy9jo75GoZzot92tVsG3qxrpWAbeKysj3j1IKtI+uVsBJRd+5GlAjeR/6OmOTslpFRLJlo1vnj98UaxgTJaVUdEYqk5Uor1Ic8pbj2fjiWjo2bOWI//00zRe8d2T7SZ5nUizXLbNjk6tUOivp+0kKzSLgXZnEUqOte7ccaYFWq4j4nkS3d4PBYNglsKypzYAz2XaAaKlf/vKX/OAHP+BjH/vYNpf9/ve/j9aaww47bEz7GvGIbysrW2s9rtt04Ne//jVnn332sH2b29ra+OIXvxg9vvXWW4dc7stf/jJu4Dx6zTXXRAI5JJ/Pc8011wBSb/ylL31pyO2EQru1tZXPf/7zg15funQpl112GQAvvvgiP//5z7d1egZDzMBWOH7CKddOxf12JwzFkKJbWbI/w6hRSw7AOvpU7MPfiHXYySKEnbRkAYBEQhvbYMHe+OtXxEIwnUO1zoNqGV2W6L5SFtjbiyLrOhdvlcpCsRXV2CbCq1YZ3wkNFNnJiwG3iu7vge4OcGuopQeTO+1czrj957zn9p9x4PtGKJChPsU4TFVOpprbqWlp8KVsB9U6HzVnMSo/hjQ7pWSMiy2T1ivcYDAYDIbJ5txzz0VrzeWXX84f/vCHYZf7/e9/z3//93+jlOIf/uEfxrSvEdUk33PPPXWPTzzxxGFfGw/J7U5H+vr6KBZlBv+0007jN7/5Td3rWmsWLlzI2rVr2XfffVm2bNmw29p333157rnnWLBgAatWraqLpD///PPss88+AHzwgx+M+k4PZP369cybNw+QD81wKeAzqdbDMEXUKpJ2HX7uUmGUbhJmKn0fyj2Dn882mJnRsVLqRbuVeOqh0ByJO+250vIojBRXy2A5KNuK3l/t++DVUHZq5K0lureADiK+dmi0JpFepSxxXx4P2g+iuPHxaO2jl/8N3dcBSqGa2yFbQAWvKSeNattt9PthwDlrLTfTZsNgMBimnJl0nRrVJH/snVNfk/zFW2bEGE0mWmtOOukk7r33XizL4s1vfjOnn346ixcvBuDll1/mV7/6Fb/97W/xfZ8TTjiBP/3pT2Pa14jyc7clXqe7sJ1IKpU4WjJUxHnFihVRbfP2xuXEE0/kueeeY82aNaxcuZLdd///2XvvMMuqMt//++5wYoXO3TQ0NMGWYMIBFUG5A4aBcVSu1zGMI4yIOmPOOnjvOHdGkPEq5hlRGMzKiBHF34gikoODMgotQUIHGjpWPGGH9/fHu9bea+9zTtWpqlOnq6rX53mq+4Qd1kl7rTd938OT50wV7amOs27dOmzatAn33nsvbrzxxq5fh+UAIgxSwSy/JGmtzXqrIeC4qYGcCBlFEmXrJGRkHstxpabU9SQN2Dw+6Uiy4Y+jOaYpafExLTZ2oNU1uy5IB3aJMs4NMt4LbtalDzXHYL8owl36OxEFYL8E0pHUOO78eTNL3a7nZ7INksT8OURfuTYqYmOOi/ixRxH+9g54J54EGhgA3/Yr0KZjQOUB0MAKwE8j3sRd1FHniSPpPxyHEhkvVsVojqPUSHc9aeNljWWLxWKxdKLfTlU7JwGQMqzvfve7eMlLXoKbbroJV111Fa666qqW7ZgZp5xyCq688spZn8uGcWaAGTU/5phjWp6/++67k9tHH330lMcyn89HnGdznC1btmBiYmLKbS0HGFqUKVbK0o1JEUiKQ2XQ+mJkFCtZIzNsKrXrUPZvV6/KLCJI+lhBPVUOTtrUKIhEIMn1pTbSK4hxMhe0+FgciijYAinb6Bt+Sd5DX7W0aTN5MsfAyOPy2ejPvzEpnw8RyC/KZxs20vey0+cNyDnCIJt2DcjnOsuaZA4DYPc2ZaDGcNasQeF5Z4CqFXmsWATKA6DBFdlUaa8g92eYrs/1cVFWZxaV76YymDkGQ71nQUO+2xaLxWKxWBYcK1euxHXXXYcvfvGLOOmkk+B5XlLC63kenv3sZ+Oyyy7DtddeO62411QcYOGX2RPHMT760Y8m9//yL/+yZZutW7cmtw855JApj7dhQ5oimO/fNZvjMDO2bt2apGm3Y3R0NHO/WCyiWLQCLksW03BkBqImMrXBRCLmM9V++n5bB2YHw7Sdweq4szak2pIXetKpsgcKRNO38GFGu89IizAmt0GZj5fDEOQXZBulpkyeDwyukqg0ORK9j4JUEGq2773+HJP9xTOvx+g87dnyOuNQvR4HKJbkvABopr6ROFZp6AxWPmJW6dekx+G4VlDOYrFYLFND6HMkuX+nWgy4rotzzz0X5557LqIowu7du0FEWLFiRUd9qZliI8ldcvHFF+O2224DIO2Y2imljY2ldZe6drkT1WoaSRsfH5+X4+TZsGEDhoeHk78LL7xwyu0tixxTGCu5kGvDiTor3HqFNH23k5AXUfsereTMPAV2NuQnpgPJQO4SclyJMss9MTZ1+yaw9CBGVkGamzVg9xb5f882YM9WYOdDIvJVKAHL1gLDq8UwLg+J42Mu773rgWsTqfBXJk3fAQaWgYplUKkKkANyHJDjgZQhDXeGU9jEiPI2G1UAGmUYM7NSCrdYLBaLxbKQOPzww3HkkUfi/vvvTx5zXRdr1qzB6tWre2YgAzaS3BXXXXcdPvCBDwAA1qxZ01FIy2xUXShMbSiYEdxarTYvx8mzZcuWTLG/jSIvcbSirVYmbkzIY8xSc9mpjtRx5Hl9jE74qva4aXzv+pX27BXT3raOZ43kDlB1GZgcIA6TCCqVh8UQDOpK2TxXLw4GamPZz7U+JinzvWZyBCgUQdVhcKCMdr8gwmKG+JgYzCrlWpcMADNXR89nM2hruVmTmm39m7Cq6xaLxWKZCluTvF949NFHUSgU2rbP7TVdGcmve90MWmzMEiLCpZdeOu/nmSm///3vcdZZZyEMQ5RKJfzHf/xHx6bUpVIaWWs2m1Me1xQBy7eJyh/HvD+T4+QZGho6oBXxDliIVForp+rBU/Uu1vt0g44aJwYVS33rfPdh9XzAGZDXswBb9iwkyPOBQKU1Oy7gOKAoNFp/pd8FKpQlilwopzXnQPusgV5QrMBZcRDQbCQOHA6a4JGd0sKKOR2drh+OIyQh4JYe39PgF5QyNqukCgYxwG4hMZAJ6Ni32WKxWCwWy/5j/fr12LlzZ1/O1ZWRfPnll2daFM0XC81IfvDBB/GCF7wAe/fuheu6+Na3voXnPve5HbcfHBxMbk+X+myKbOVTqvPHmcpInuo4FksCm4t+7m3NZd5I7Ze30xrH3eGXVMo8p06N3GeUuE2YgcHVoPIA2CsCjXF5n0uDmA/IK4Dr4+kASGqDafUGxEEdACcZ0Zw4e8wDzOy7RnGcnCPJenBcUGMiTbG2baAsFovFYlmQPO95z8Oll16KO++8E8cff/y8nqvrdOsu2iknmMIw3T7fDyN8Jmzfvh3Pe97zsH37dhARLrvsMrzkJS+Zch9TZMsU32qHKdZlini1O86qVaumPQ4RTSvytWTR6cREvWsFFEViWOo+suZ5HKd/BlocSbTM9VrHMZPXS/NoyDquRB7DpozHpqp2JhIlZTieREWT9llh+p2KI7kdR6olkZ8KVzHL4zNpe0Vt6s8dV4znsKnaf5EYkLoFVBiAmjo9P5bUbFItl/Q4k+8hOqa88/g+SWUGIbz7Hjir18I97FDw2F7AcRHt3AW3CFAcSYp/qZIcn3KOnMwcoc/XTnhuKsI64BbSsoPKUFoPHTRSxXfPlqJYFijM6nqA7Lyw2InCtAVbMtfF6bXROkV7RxzLd8jx5taK8QBHNDL69/7181wLmQ984AP41re+hbe85S342c9+hkqlh6KwObpaZf37v//7tNs88sgj+MhHPpKkGZ900kk46aSTcOihh6JarWJiYgJbtmzBzTffjJtvvhmA1MT+/d//PQ499NA5vITes2vXLjz/+c/HH//4RwDAZz7zGbz2ta+ddr9jjz02ub158+YptzWfz7eTyh/naU972rTH2bBhQ0bE64CBWWoU9YK6UJ67cFQYSP0uIBG4soqimecpVubfGAyDtO6WKI3mNSbSiFq3r1erEcehMnR6LEfgFfoj2LWYCRrp9wpkiEa1WeTGUWvdrRZQi0JgYp+IaM3WKaRbHYGBKM7WqIdNYHxvpiextI5S9cvDq2Rb3U4KEIM7p7bNO7eAH70PKA2AiOAdtALO2nVyfMTgRx+ESw4oLIJ37QBv/i84z/5z8Or1QLMBro0lvz9yfeUkiOU7P9t0/soyee+IgMGV6W8haCSp6GD13ltnj2UhEjTEaQXId3Q+tAL6TdhMS3YcN20RWBtL59zSYO+c4AcSusuBnj/iWDQmNKXB1FBWSv9LxvFiWZJ4nocvfOELeOMb34gnPelJeOtb34pnP/vZWLNmzZSiXbOxNbu64px99tlTPn/rrbfiXe96F4IgwAte8AJ85jOfwROe8ISO299///1429vehp/+9Kf41Kc+hR//+Md45jOfObORzxMjIyN44QtfmPQq/uhHP4o3v/nNXe17+OGHY/369di+fXump3I7fvWrXwEADj74YGzcuDHz3CmnnJLcvu666/DKV76y7TF27NiBe++9FwBw8skndzXGJUXeQAbEsJyrsWb2+VX9W5MFuj5v2JzfRbS5aNDnjCOZzMyU0yjs7vUSpca+Zf8QNow7nKQXJ2rjJi01scbzrieL5KAx+0WjrlHX6GhNswZMjKTbOK6q+3YApyALrGZDIr7m7yRstBrJ+x4DlBI1ANCylWBmUaYuD4ILJdCqQ0CuJwbrijXAYccCo48D9XHxmg+tBvWydVix0ire5TjAwHK5lpjvhzWSLQuR0NA7yfekX6yYr0Nf++IoO7dHgTWSZ0rYTB2zhYo4FxMtCkUcyrXddL4UKpJRZJmGfjsUrPMCEFtLMzExgfe85z3T7kNECMNw2u3yzDl2v3fvXrz85S/Hvn378MpXvhJXX331lAYyABx11FH48Y9/jFe96lXYs2cPXv7yl2PPnj1zHcqcmZycxJ//+Z/jv/7rvwAA559/Pt7//vd3vT8RJSnZmzdvxi233NJ2u1tuuSWJAL/kJS9pSTXftGlTEl2+4oorMDk52fY4l19+eXL7rLPO6nqci5IoFK9ybUxux1HWy6yZ6yQaBrLg14YxSASM6uNpDaNO1ZwrcSzHrY3KeU2a9dzGKi0XlG3JZBcNiwd3Js4b45rAyT/qvro9l0VMPnXRceV7Pmn0Uo8iIM59zx1H+m1P7G3tw23AkyNAZQBwfSmtKVYAv5S51tG6IyQq7PqgyhBwyLGgUgVYfRiw/gnAwUf31kCeCnKyi51eZ1pYLL3CvOYvFUeO+XvT85ue7zR2rpsZQd3IXEI2Um9CjjwX1LP7WiwLFGnjOLO/eKYinwrimRQbt+FjH/sY3v/+92NgYADbtm3LiE5Nx9jYGA4++GBMTEzgggsumJFB2muazSb+4i/+Av/5n/8JAHj729+OT37ykzM+zr333otjjz0WURThhBNOwK9+9auM6nStVsNzn/tc3HHHHfA8D3fffXdbp8Jll12Gc889FwDw5je/GZ/97Gczzz/wwAN4+tOfjtHRURx11FG455574HntJ5HR0VEMDw9jZGRk8apbmwYxOQAoJ0YF8ZJ6xbl59szFvz4PzB+XivwVynNX/G1MqBRWRWlQDJA4znnRlYHsemnUOApUTfISWSQdCMSxfObMrd9dvRj0i+AolDT7pDaXwY4HU8QKQUN6IM/FiIzjNDrDDIztlse1YrjjtP8taSdR8pz6v7pMdo8jYMf96lAMxAxalusIYDh6EsO5OAB0uIb1BV3/SI5dkFsWLsxp5NXUzFjsREGqwaBfk75G6fnP0h1xJM53E31dI/VexlF67a+NqWu63q7/afyLaZ2qx7rnA6/GUKl/ZWaj9SZWfPQbi+I9mk8efvjhWe132GGHzXifOV91vvOd74CIcNppp83IQAZExfm0007DD3/4Q1x55ZX71Uh+1atelRjIp512Gs4991z87ne/67h9oVDApk2bWh7ftGkT3vve9+KjH/0o7rjjDpx88sl4//vfjyOPPBIPPPAALrroItx5550AgPe+970do+5nn302LrvsMtx444343Oc+hx07duC8887D8uXLcdttt+Gf/umfMDo6Csdx8OlPf7qjgbxkyUeQ5yU9KG8gKxxPnssYCj0gbGZTcl1fXqeuT42V6JOexKyIw+IiqKWLW/N7Q64oKzse2HGBfY9LOrM2JEWKOd2PWVKb56pQ7jiAo2p7TWeN48pvKeM/7Rw1bvkNRFEizEhEQKksrztUondeISnJThS1ifb/95kcW1dvWfhoAb2lRjuHr3mNsnRPy9yg5o0wABAAMDQkgoZRSgbAIaA4Q0HEAxXbJ3m/MBtjd7bM2bJ68MEHAQDr1q2b1f5r164FADz00ENzHcqc+O53v5vc/sUvfoGnPOUpU25/2GGHdRzzRz7yETz++OO47LLLcOedd7atKT733HPxz//8zx2P77ouvv/97+PMM8/E7bffjiuvvBJXXnllZptisYjPfvazOOOMM6Yc65KgWBGhIECiuM1adrHeKy9zccBIUWqTZGGq/Ubh3LytfhngSXkdfqm1vsx1gWbuMV2PFtSzghuWhY+Z7qOjtUQiUpOIZgUwa5STel5yDAEWNVH2Uu3V9aRmvT6R1iEn0ZworU/OT9LU5vvHMbBvJ2j5msShRGEgcfCG9GCmOJZ+zWCwjhZVhmzFlcUyX2hdjXxpgWXpkb8uu35Oa8XMZMp9F9xC++u6xXIAMudfgu4H/Oijj85q/x07dmSOsxRwHAeXXnopfvzjH+MlL3kJ1q9fj0KhgPXr1+MlL3kJfvKTn+BLX/oSnGkMnFWrVuGmm27C5z//eZxyyilYuXIlSqUSjjjiCJx33nn49a9/jde//vV9elX7GceVRXxZKVzmRIIyNcNzwfOn8NLnxZVmLgKQwXFEVbg8KOc1FXtdv7UeNE9Lyq5lQWN+Z/2SpEuXh7LGruuJQ0jVhKl4rLrNqd+Gua0PZ04UK0BlMPc159YIa6YcoRUqlIDqMvDenWIQE0k9cmUQGFqpDH/VgkQb4M1aa12+xWLpDczi/NUaGC3CgJYlhc42kzsy9yTXcMq2uPP8NMhAzuw7BxyI6EhyP/8sLezcuRN33HFHIojcS+Zck3zkkUfiwQcfRLVaxdatWzE8PNz1viMjIzjkkEMwOTmJjRs34oEHHpjLUCwdWEy1Hl1hqjCa9KL9E5BtAQVAapCrUi8ZGCnRrj+3mtB2xLGIdsWBTFgdU2oJKA9Yj+9iI9+Oox2NSbASWSEgXcAoAYpkmtQOo14NbXwvMDmiWrCUQYlyJ6Vp3lGoyho4fVxTSa/9PDkKTOxNYuKk65VVr2cy1LWZHPnNDa0SpWuLxdJb4iir3u4VZt5j3LK4UNfaxLgK6koQVK0dTOdsftv9wGJapyY1yX//mv7XJF/wtUXxHvWDH/7wh/jwhz+M3/72twDQomC9d+9evOpVrwIAfPvb356RfaqZ8wr79NNPByDK0G984xsxE5v7TW96EyYmxBg57bTT5joUy4GATnVuR6/STz1fDBAtzuV4IqjFLF7WYlWM414vMpilbjVWETWOW+u03ILUX1sDefHBsZQM1HOCbc2a9CWuj8vjzRqUaSpEUfJZk+lRnmtNsjm0MBADGZAFddBssxUpoZc4Nfb1bUp/exxHIoAHER0DOeBkn7QWmVUNNhGBtFFusVh6T97w6WWphmVhonUe9FyRdMxgCTREocw59Qm535iQtVUvMvIOFByn/38WANKe96yzzsJvfvObjIq1yfLly1Eul/Gzn/0M3/nOd2Z1njm/43/7t3+bNG/+j//4D/zZn/0Z/vCHP0y5z7333oszzjgDV1xxhQzCcfB3f/d3cx2K5YCgw0LarOvsFmWQiKhQMytg4bhiEEdNSQuNVHSZlKEwH6qieuIyaWnVQ2LEWwN5cRFH0l4pbMh3qTam1K4nJf0xUv0sw3bGaafIcw+/f/nvshbB0xEGBkTELreA8oqAX0K4czcmvvxFBHf/Frzl/uyE5flJwnjyp7/XROCkxtoayRbLvECqtEdHkG1XhAOMNtdW7ayNAuWcD5XWii17sSxsbrnlFpx//vnwPA8XX3wxdu3alehb5XnNa14DZsbPfvazWZ1rzrltxx9/PN7//vfjggsuABHhmmuuwbHHHos/+ZM/wUknnYRDDz0UlUoFk5OTeOSRR3DLLbfgjjvuAIBkEfW+970Pxx9//FyHYjkQIJJIajPXO3qmaZpBIz1GACSpo2FTFhN64T5FL9gpYZZJh9wZeP/yPWlVfTSRjNdxs7VElsUBM3h8DwBKI8FgWZDU81oMLN+//OOFMtCIlROHsnVkPYBcDzy4Sgx2rwAMrhA16uZkmvaf//oXq4BXQLT9YYy/702ovOEt8NavU6+tKb8ls20ZoFqbMcjxksORfn3W8WOxzB+Oa1OsD1S0QGRQV3XHpQ4OWfQ0Q8limQ8+9alPAQA++MEP4u1vf/uU25566qkAkHQVmik9WWX98z//M+I4xkUXXZQYvr/+9a/x61//uu32ZnuQ97znPfjIRz7Si2FYDhQ8H6BqWjc8037FTZXymjEyVPVkS2upcnqebhcYSd9BdazSQHcGjVdIe0WaNWNeYWm2/DhAYI5FzRkAq76UVKyAldicVrBmIpBfTlPkamNygNKAPFaeWYu9mULlAUnj1/gF+QubknGh24gAIjjmeuCtD4C//nEMX/gx0Or1aV2bXwQHTfD4XjhVsw6IEpVsMs1k+/22WCyW+cPzs20ydZcQfU2OQ+WIt9firrEtoPYLN954IwDgLW95y7Tbrlq1CtVqFdu3b5/VuXrmur/gggtw7bXX4qSTTgKATI54/g8Anv3sZ+MXv/gFLrrool4NwXIg4XqyUC8PzVyNsVkz6iqRjRA7XvZC5BWAyjL563byiKOssd2Y6E6513GlrVN5yHr8lxJxLDW/gHwPauNAoQyeHEs24bAp4nA668ArAAMr5G9/q426qi84OZIZURlOnD589+2gFWvFQM5BfgFYfhAyqX7tFhV5pXqLxWKxzC9+Ua7llSFxwFaWydrDZvRYFjiPP/44BgcHsWrVqq62LxaLaDY7ZE5MQ0+lRJ/73OfixhtvxObNm3HttdfizjvvxM6dOzE+Po6BgQGsXr0axx9/PP70T/8URx99dC9PbTkQma1XzfXTWhxzQiiU26tVd3ueSKWnJtJExn5BPevF7YT1FC49HCebSaCMXiqmjhDyCuC8E2ahfBdIRbHb9Ermgw8HmmPgSKIQSVRcq1hzJKnbcQSEIShQ/c0JkNYkFYlWWywWi6W/ELW/bekOG0neL1SrVYyNjSGKokQTqxPj4+PYt28fVq9ePatzzUu/jaOPPtoawZb9C7OkicaxLMIdV+5HoSz6C2WJivlFeVz3B5zLRagxRa9me3E7cNm7A3BcMRxJUts4CiWqzAxW3zt+5G5wqQJatgZUrEg6c21UxL0KZZBXnPt3dDo4lkh3HAKOB/ZL4viJQhnj2B5Ev78L4d2b4R17NOI9j8PdeEgixMUqfVzXXjMcUecOm3Jsx5XfiOvJbzOsy7n8gmRxdByXquGOY4myhw0AJE4t+9uyWCyWmaPXSbrMSwuTevM8z1gsc+CJT3wibr31Vtx1113T6ll9//vfRxzHeNrTnjarc9m8CsvSRNdRhg2p7QyaqZK1Eg8SlWpPtXMqzX1S6GggOxIxsxxw8L6diO+9HQCSKCu5HrBri3wfVe/KeN/jQH0czuAy+c5O7gPXxoHd28SwjEJwc7Jz+7NeMb4XqI+JURzUgcl9wMRoovBOlUE4a1aAwglMXPgRcBCBqstTwxhZfS8qlMR5pFXidSlCUFePRelvtMPvh+MYPL4X3JgEB3Xw5Ai4MSllDLWxtvtYLBaLZRqChlonBXLND5vSKqoxOf2+lkXL+973vmTO/ud//ueO211zzTU488wzsWrVKpTLZRx99NE4//zzMT6eFxztLy9+8YvBzLjwwgun3G7r1q34wAc+ACLCy172slmdyxrJlqUFs4pQqfoDbfhGjTbb9ljFsVhFUrupW2w4Xiq8ZDng4MlRqUl+9I8SPWalXp0oixJQGYS7egPo0GOSSCwAMR6JQEYaM6JQvt/z1csyDGAKdBEAOE4yoeqexu4TN6H85nehdNZr4B7+JMRje8FxrBZagRjKXkHG2gKp2n83px7f4fe4/T5R+2aW/tFESUFDS8s0i8VisXSHbvek04Z1+Vkc7b8xLSaS962Pf3Pkpptuwsc//vF0TdGBiy++GM9//vPx05/+FMcddxz+4i/+AiMjI7jgggtwwgknYNeuXXMey2x5y1vegoMPPhhXXnklXvva1+J3v/td8lwQBLjvvvvwiU98An/yJ3+C7du3Y9OmTTj77LNnda55Sbf+zW9+gxtuuAFbtmzB3r17EUURLr300vk4lcWSwizRpfwFngjiD8otwmfaV3k6rAq1JQetXA/e+gfw3h3g+gScp/6pZC8MLJeoreeBzO+hKRxXKANju8HNGkgLuXEskV7d97TXKXHFSrpw4lhs2CgAl6ogxwFHEVAZgPeUE4Dhdel+fgU01EZEo9km8t1uzI7XVjCG41jeo/JAIvrIYQBynFQF3mKxWCwzh5GmWbc8YVlqTE5O4pxzzsFBBx2EE088Ed///vfbbnfnnXfi3e9+N1zXxY9+9COcccYZyf4vfvGL8fOf/xxvetOb8J3vfKePo08ZGBjAj370I7zwhS/E1772NXz9619PniuVUiFQZsb69evx/e9/H74/u97wPTWSv/Od7+DDH/4w7rnnnuQxHRnJG8mPPfYYjj/+eIRhiFNOOQXf/e53ezkUy4GITufUJBd+kshunIs6WW+pZZ6hYhnOn7xAWo6VB8W4A0DDa8DVZQBIpba1LlTIccHrjgLCBtj1QXGUpltzLMZsr41EjtNxkCf9jF0vHbfrgnXrkKgJeJ56vMPxPB8Iaun9YiVnOKvfZrHa2eCvLku3JYD8IuI//gZYdwRofyt/WywWy2LG1h7PHsfpb5bgHM/1wQ9+EPfddx9+/OMf44orrui43YUXXghmxt/8zd8kBjIAVCoVXHrppTjiiCNw5ZVXYvPmzftNf+ppT3safvvb3+L888/HN7/5TdTrWYd8oVDAq1/9alxwwQVYt25dh6NMT88+3Te96U14xStegXvuuael3VM71q5di9NPPx27du3CD3/4Q2zbtq1XQ7EcaMSRGBpKCCnBL0o0rjyAtp7RmbY6iEI5T1Cfv3RXy5KDXA9UHU4MzeRxrwDyfKA8CPaKov6MtI88GjXQxF5QbRxUGxXD1Ey17nWrjrAJ5ji5dsf7diPa/kBimCf97V1P2oWAwEFDaqfbpT1rURhykSi9x5H0NSdH/ZHoBYQd2jOEDQBqLiEV+IhjYPlaaTHV62wQi8ViWeqETXHcOrpwxVjPEE3h9bQsVn75y1/iM5/5DF772tfizDPP7Lhds9nEj3/8YwDAq1/96pbnDzvsMJx88skAgO9973vzM9gpiOMYd999N2655RaMjo7i0ksvxd69e3HDDTfgiiuuwDe/+U1ce+212LNnDy677LI5GchAjyLJH/rQh3DJJZck91/4whfitNNOw09/+lNce+21Hfd77Wtfi69//etgZvzkJz/Beeed14vhWJYSkYoOe357o4A5qVcEIKmspFruTBVlc6Z5vt15GhNqTABA+79/rWVp4DjA2C75ntMAxBqMUiMTALigvtuqXph77P2PY0n/DhppqzKPQOUyuDYmhmmjBp4cB61cl7axIgKDgHZtGBqTiWqqNERjiSI7btIgjTkGwKDRXdJT2XQkRAEoqINJaqK5PinvQVAXQTDXB0rV3r4PFovFslTRXQICQ6PFLcjF2PVSTYpCudMRLCaLpAXU+Pg4Xve612Ht2rX45Cc/OeW29957LyYnRbjthBNOaLvNCSecgOuvvx533nnnrMYzG4IgwIc+9CF84QtfwNhYKti5YsUKvOMd78Df//3fT1tnPRvmbCTfd999+Jd/+RcAwLJly/Dd734X/+N//A8AwCOPPDKlkXzaaaehWq1icnISv/zlL62RbMmilH+T2+Wh7EUijiU9NB/Vbdfr2Csog1tFvJRab9eGcv4cvRb9shzYRIG0XIpC40LPqVHc7uIfx0CvHP6sSg+YkwUUkaSSMTMoaoKqQ+ByVR7X42GWyHK73wPHYgwroS2dLq1uyb7kyJ1CWdK3nZJEjSf2AZ6fCHUBAE+MgIZWygFKA8DgSpsqaLFYLN3ArDLhGuKM1HOL60p2j6nnEjasobyAGR0dzdwvFosoFjsHbd7znvfgwQcfxPe+9z0sX758ymM/+OCDAMSeGxwcbLvNhg0bMtv2g5e+9KX46U9/2pKhvHv3bvyf//N/cN999+Hyyy/v+XnnnK93ySWXIAxlYffFL34xMZC7wXVdPOUpTwEz4/e///1ch2JZSpgGMqCiZ8ZCPGyKgFFjIpty6adF+xmoTeS3U4pnp/21YjXICgZZesvgSjESYWREeAWJlHq+9Bo2cdw04tsDmFxwbSI5JjMjvn+zPEkk6dV+EVQoSfRXL6aIVF1WG2vdL+X6QVHmLsNILXccYGIEHAaIf/MLxDd+H/FNPzSed+FsOAa0bC1o9WGg4TXz4jW2WCyWJYc2kKMgG/0kQuK5NEtmbNeABc2GDRswPDyc/E3VCuk///M/8YUvfAGvfOUr8dKXvnTaY+sobbXaOUtrYGAAQKuxPl/8x3/8B66++mowM4466ih88IMfxOc+9zm8973vxfr168HM+OpXv4rrrruu5+eecyT55z//OQDgyCOPnFUfqo0bN+Lmm2/G1q1b5zoUy1IiyLVsIgcASWp1XnCLY6AyJM9PuXDOPTeTsmIiiVBz3MV5LJaZQZVhcGkgjRqb33O/KFkUDKlL5qj3tbj1cfmrDAKuBwoDcNFF9Jub4R7/nLSemhyQ60omRkaVu42/1fOBQhk8sQfwS2kv5XZZGaz+r4/DOfKp4IFh8IN3S4/mqvptWywWi2Vm6JI0c91kZic5ygxw/bSzQQ8dsEua/ZRuvWXLFgwNDSUPd4oij4yM4Nxzz8Xq1avxmc98pi9DnA++9rWvAQBe8IIX4Ac/+EHm9Z5//vk47bTTcOedd+LrX/86Tj311J6ee85G8sMPPwwiwjOf+cxZ7a8/aDPH3HKAwqzEiSJlFKtUaseTiFrYbK9IrUWApiN/LZvNta3XYkkWiyLTCkpnTRiRVBGmU9//mZYLTIdXAK06WJ2SAb8IZ9PTgUPHAb+UTXEiApGXXWh1ELIjqDZNHAOxrkRWXQ/ASLoxE5L2V+R6oPVHIQ4kxRuAvNb5UPO2WCyWpUocA43xbL/6vFEX1IAmxBnr+QDICiIucIaGhjJGcife8Y53YOvWrfj2t7+NVavatGhsg06xnpiY6LjN+Ph4Mo5+8F//9V8gIlx88cUtDoGhoSFcdNFFeP7znz8vNdJzNpL1G6nD7zNFF4ibva0sByjNmlKzVTi+qC8Wyh16q6oLebe1M/lj2InAsmBxAA6z6XAtP4HeebDJ88H11NAlxwGDQNVl4KAB0q2hYgYnjiJO/2s3FC0SAxi/PTGsiRPzWExl1a8zc5hV65MWgki2tlgsFktX6DpjcgyHq5dth6lTqxshUBm2WXIzYYG3gPre974Hz/Pw+c9/Hp///Oczz23eLOVUl156Ka655hqsW7cO3/rWt7Bx40YAwL59+zA2Nta2LnnLli0AkGw73+zatQulUgnHHHNM2+e1wNiuXbt6fu45G8krV67Ejh07sHPnzlnt/8ADDwAAVq9ePdehWBY7mWiUVviFLLSLFYkixbG6wFMaVW7WRMhnOsgBChU5nuN2rl9e7DCn742rfuJRCICTaN28ndPpMqrfi3MRybn6dd5+Uh6QFh1g6SEMyGfnFyWi7PrpZ8ssny/BqJuHvC9RCHg+OIqAOACYED/0AKgyADr4UJW+zeC9O4Hf/Ap03DOA6hC4WAY56nsSNiXCoNowAch+hyj5J0tjXH5rWm1ep1o7noxFR5LjCLxzC3j3DjhHP0OdsyF9oTlWzjIVuQ6DtJezPm43RCE4CqXdlF/IGN6stAlIR7xdr/vjWiyWpUWsOgs47sI1GKcbYyJqmstI0tossZemVrOZndfJ42lZrIRhOGWt7kMPPYSHHnoIhx12GADgiU98IiqVCiYnJ3HHHXfgT//0T1v2ueOOOwAAT3/60+dn0DkajcaUrZyGh4eT7XrNnFeVRx55JJgZt91224z33b17N+644w4QEZ761KfOdSiWxU6hlBOUUJgiQcUyUB7MXsfbpWB3QvWlRbGycCfAuRLURdSsPibiZ41JYHIfMDmStrHKwzz73s/MqYhabWxmn8dsaE7KuerjQG3UOO8SUhx3XKmzrwynBhuROHbKg9nfSmMcmNgDjO8x2pSF4JFdwPhe8MhOYO92YHwENDkCKvmo/9O7wY/cA+zZBt69BbzrEfDmXyO+8l8Rf+tTwOgeAACP7gY/shk8MZqMi9o5I9r9lkK1CItjER5jVo6qctq6iQE0JuGsXA/nqKfJmMOGpJ4XyioCEouoWWMi/dOfvSnu14lmHTy6CzSxD6iPg+rj8luYHJHfRXNSUg71b2Z0Z6smgsViWfqETZlLtEbDbOfE+SRsytgaE+LkzBMF6XxsZvAAqQOwUGpdB+n+9ZYlw759+8DMbf/OPvtsAMA//dM/gZnx0EMPAQAKhQL+/M//HADwjW98o+WYDz/8MG666SYAwFlnndWfF7IfmfMv4gUveAEAYNu2bfjBD34wo30/+tGPotkUL/7zn//8uQ7FsthxXKA8LEq6rlF72K4O0XzMtSITGcwFftgQI6Ddc5o4koVBt0ZHHtULNz1nMPNjdIuOmnYaxwEIN9LPl+tiJHOzlkQRiFlSp9ViyBkaRvkjnwSVSuA9j4HvuQO0cj2wUnlqwwAxOYi33Q/efr+0cipWgOpyMdor6jfqlyDeKmqflaHLJBwS55Te31UR8YEVQLEC0qrzRKDla0GlqjxfKIKSz9qMXDvpeYPpv69cG0u+n+Q4bZwp6likavH8YvvFp8ViWbqEzezvXmeyLDTMrhwtc29TMpD0Y8xAaVCu15VhyUoyHZqOK6KQlWExnC0zQ3oU9vGvPy/rAx/4AIgI//7v/46f/vSnyeOTk5M499xzEUURXvayl+Hoo4/uz4D2I3POKTvnnHNwwQUXoNFo4O/+7u/w1Kc+tas89S9/+cv4xCc+ASLC8uXL8dd//ddzHYplKaAv4IUSEPvpwjWPV5DHdcqRJcX1cgajVH4mz+UJGunzYQPwCzPzKLc42+fZ+66ji3kOpO8Bx7IYUml3mXd8fE8SzU8Et/btBK84CHAcmWc9Hxw0Ed93F5yjngKqDoJedA6wcxt4xXo4aw4G80ZgfESMVbP3uBl5cKf4jRbK8jzH6XYmRCC/CFbfT3KVBoF+jmF8Xzn7nWynkt3praqNwilW5b1Qi420GZUIhaWLD/VbWapZJhbLgUwcAY0aAFbXJy/VTsi3hNQlHf2AWZzZcSzX1anUpckFoLPrjJ71QSM18k0dh+nSxu21zpLj6U9/Oj7+8Y/jXe96F84880yceuqpWLNmDa6//no8+uijeOITn4h/+7d/6+uYHnvsMbhu5zUeEU25DREhDGfe2mzOV4BDDjkE73nPe8DM2LFjB0488UR89rOfxe7du1u2rdfr+MUvfoH/9b/+F173utclC7h//Md/nLInl+UAhEgmsKkMH8dNax0tKcWqLAB0dM9xZTL1VCuhPC0LgRm+n/mL0nwaq0SSqps/R6FyYNWR1icl6h82ATiSzhyJ0BdHoarbDsHNOuLHHgIcFzy6CzDNQ9cFlSvgoA6A5HNcdyhouehDEDmgweVZA9mkm9+o7vfc6TfqOKCBZaB2C0NTOZu59XtKlLYvmYo4Rjy2G+T5IL8Icj2w44KJwJ4HVuJoqWxYHxfHFotlfohCKTUKG6lDrVETTZM4knRkbZwqbYZEsd9xJQLbr7VFUJfxcizjmcoBaNYQc5xqdJhZNaxqi0tVuz6aT/oaRaa+fpbvfOc78bOf/QwvfOELcdddd+EHP/gBBgYG8MEPfhC3335712rZvaJT2vhM/mYD8Wz3zA3+5S9/Ob773e8agiiS295oNEBEGBoayjSe1qc955xzcNlll811CJYpGB0dxfDwMEZGRvom2W5ZIEQB0GzIxdXz20f0tCc9jiXVdDbGZtiUP9cTY7zdOaJQqUH2wIjuxZgXM+N7k8hHqhONRGyLlcgVb78fqAyCVh+a3T8KgZHd4D3bENeacA7eCBoYlsXWivUiYtUP4ljqqfVrICf10ZCTTa0vViRaoheyHIuRXJn6msZRCN6zHU5ZqXSqxQY366mjDcjMXXALon9gsVgWH3EM1EbS+8WqOOsymhkkWVNB3RC2UmrFnnLa9csoadaykezyYGdHXX0iq05dHlLzfC0r0FUa7K/y8hxZTOtUPdY9F74JQ6X2PYrn5bz1BlZ88N8WxXvUS/7xH/+xJ8f5h3/4hxnv05OVJRHhiiuuwIc//GFceOGFiCK5CDWbzWThMTIyktnHdV186EMfmtWgLRZLl7g+4EUqFSsE3FDVHhlROrAybDH7CJpX6NzDllmJoKgU6UJl6nSybiDqvvXXUsRYvBEAHlgOhE3w3kdlgRWH0uv48CeDY4k2kLFg4jAAlq2Gs+YQacdUnxCDNAr663BoUVZFKrCaT6lv1rLphYyuROIojkDlAeP4DMQs4mDm9z3pz45Ftbi0WCw58teOKFTdAUqpMVkopYapmZ7MLEJ+YUO6ZvTDUPaKaSR5OgGtQkki5Po16BZ6QDpWr2ivYf2gz9HdAzUrYH/aiT37FTmOg//7f/8v7r//frz97W/Hpk2b2oa5N2zYgL/927/FH/7wB2sgWyz9wBTSioJUpKs2Kt72+rioYdbGZPLtNczZRcsBKrDVU4wUaHZcYO+jwOgu+ax3bQUa9WRCFbGqKBWsimPwnkdBBcMDHoeA68iiazbibbPFMdstUbbNFGAsFgmZJ3Q0uQu4WcumWunUbV2LnLRKMRxH8yk+Z7FY5hfHNTKWSAzQ+pgYxaWq/NSbk9l5yStknbc6lbkv43XEuVkZlsyoKbd11bZDMsbamBj1ZrlIvzKBLJYlTs9DBocddhguvvhiXHzxxdizZw8effRRjIyMoFqtYu3atVP2urJYLPOA67WKkgBp3ZJJpPvQ9tALrQVQ9ILkQEuNng90VIQjSacH5H0eXAGsOEjuavGuOAbv2wkMDIvBzAxadQiYOU0xDhpAHIOGV4H7+fkQiaJ9HGXrBPW4tLAOURo1TtKt29Qpt6NZSxW088a1+b08MJ30FsvSQ6cbcywOr1BdI+NQnMMajgG/LNeWUJUDJQJ/TteOuP0Cc65bBYuB3a7cyWKxzIo5r4be9a53AZBI8oUXXgjfTz1xK1aswIoVK+Z6CovFMhf8khhVHLdvmdMSlevxBKvFtqIwjeBZ5kZzMq1L83xxbii1aA2rNGoe3yfGcRggDuqggRUAZIHFriep1mET8IuS6dyNGFYv0QJgcYQ0mguJhujXyKzSEEnVJcdqvy7q28kBT4wktcuUpHPnokSs/iECPPsdtVgWNUQAuYBjONeyGwBQWhn6OhNHYmguFlHQvMp/0BAj2dIfdA17P89n6StzXgl88pOfBBHh5JNPzhjIFotlgaBFuwCZ+IOGurATEDSV1QAAjogVzXRhoHs1thMFS8bgLL0UMGaVOk79X1CZglZE4K0PSqumjUenYwsC8N7HQK4LxAAKZVB1mUqt9pLvBFUGJWW7VAEVKvu3ls18Dx03K1DjuvIdc33l7CGg0MWCcHi1SkUPZXvlFGIiiTLptli6GNr17ULTYlmMMIvDjyidj1wfcANVQmEYlESA47cv/5lqLuslsepKMF2XgHYQiSBZI5c2brFYesacjeShoSGMjY1h06ZNvRiPxWKZTxw3U8+KKEhrVf1ZTNRh0+jN2Eehk4VAw4zmFpWISp9w3CT9mADE118FFEvAoU+QyCkz+Pe3gQ7ekG7v+SCOVcA0TbVmZlBJfSeC2tQtm+YTr5BGdbyiRHQ4lvuOn7Z7cpzsd9iAmaVGz/NlPTy+DxhcDqoul1RuQAzkYgXYuwPwi+JEkCdUWnco45iruJzFYukvjfHUgeiXRNwxbKQ6C0krHTdVyzchUqVJNLvrOccyn07XmxiQubc+rk8sdcYznX8dV7K0tKGss20s/cEKdy155mwkH3TQQRgbG0MQWKETi2VRofsraqIQmKldYEY0OUYqTXwAYEY5zdv9oFAGh00xkP/7VmByDJgcQ/zwZjiHbgJqE+CHfg9+5B44J71ADGQt5AWAgwbYdaXVE7N8ajp1L472T0q8Tss36dSjuQ3MDN58ixi/ROBtD4MOfxKcjUfL6zNbhQVN+b6ar9NcgMQhZv5jsFgs+xVzPooCoMHKOUZpanJpMOsAa3ftns31PAqASVXv7PqqjVOHuTBspk47c+yzaY9IjjinLRZLz5nzSuiUU07BH/7wB/z2t7/txXgsFku/IJLonF4QuLMwClwvTVdzXBwwBjKgXvsc3rs5wLVxRN/5AvDAZsABqCLGJN/9a0S3/gwch3BPPQtYtxEApynUQQMcRbJYHNsHdhx5fmiVPE896mO9P2hMioEMyOurDsEx0891WiOQ1h1GAeC0EfWydfMWy+IgjtIWg+SmbeWYW7s15DUxTFHJjLhkh+s5K6M7CmWbYiWNPjdraYZPIoDZ5loa1NPsq/TAs7/mxLESPFSR5OnUsS0WS9fMufjsnHPOAQD893//N2666aa5Hs5isfSTYkX6Fhers5tcvYJ4sfUxDqR0oEJF3r/ZvnezJH74fgQfex/iu+8Cj48CY2PgvXvRpEHgsYeB2jicjccC648AuS7I9cR1QSqF0HFlvNVhWRgWyhJNLlSA8tDi/Qz9YjZCVBsHT45Jn2jdV7sor5E8H1ixHigOgP2SfJZ+Sd6f0kDfnR4Wi2WWNOupccuR/JZJOcD0pUwbwdXh7PUtMPeN1TViiut52Ewdo1EgDuagkTV6td5HXmSJGaiNA428gUxAaWj2zsmwkb4G8/VY5p8kfb+Pf5a+Mmcj+eSTT8Yb3vAGMDP+6q/+Cg888EAvxmWxWPqBFvWaS+RM1bsecBfwRBSmf1FHZkb4yQ8BWx4Cdu0GOz6wZi2iJz8LwY7HwJCWTwiDtL2T3lftD1fV4/lFYGA54BVAjgtK+gUvTsj1QE96LnDQkaANR4Oe9Gzwji3S2qqoBMnCMOkTTkEDRATS9YM68rNYI+kWy4FIftpp1rJiXHqO06J9U+083fU8P8fFkeq3zNnWUZXh1m3DhmwLTsdBrjgmu1Hp75oDbB62WOaRnqzuPvOZz8DzPHz+85/H0572NLz1rW/FK17xCjz5yU+Gsz+VUi0Wi2WpMT6aLrI8H3TSn8KtjaHygucivO9BYM9j8MbHgccfAQ2vlsWaX5A6XEeUuJP1WxgiWVQFDdmmsoijyeVB0PqjAACkXhdFzVS4B7rVCwGREu0J6khUb5s1oLp8/yp8WyyW7lGZMNITWQn/mVE3x4VueYegoQxY9fsulKRuWfcYnu665/ppazqvYESLlRYHORKJbnf90AKZcSTPkweUB1ojzjPFL6WCYVa4q7+QM/fPb6bns/SVORvJRxxxRHLbdV1MTEzgoosuwkUXXQTf97F8+XKUy+Vpj0NENgptsVgsU0BEcF/1t4i+8TmgVIHzjJOB+ngSNXZWLkc4vA500Drw9vvBj/4R5HmgjU+Wur0VB4GIRNEalIpXJfW4+1G4azqS6Ev7RSAzA7u3pr3AHU/1CFcLGXM/s/44E1zidBFrsVgWPtowBYCRx9VjKjLreJD+dwZxDLhOum9eLHDKc1FWTJA51fVw3NbuDuY1q1CSa1MUqgjyFMJeM0G3grJYLD1nziuhhx56KJPWZ7YVaTabePzxx6c9BhvtSCwWi8XSGfe0vwCdcAr4rmslMhKFqm0J4B51DNzamPQE9XzQspXAyoMkYlEZlMd93a+aZeEWG4tIZ4EKd8WR1POB09rhPFGYGsgAwDFIG7scp4axmXKZT7903IXpILBYLO2JQmByn/yWPV+ucXGk6opzBnKvhQlJtW4ynW6aOE4FxbTI18CK9ttaLJYFSU9WA9xS59HdcxaLxWKZGfzHuxHf+QtgxUpxLuqFYaEMbHsAqAwApYr0Ql6+JoluEBHYL0hiIHO6WHSU4qtf6q6/5/6gaaREB/X2qZGum+kfDSIwxxIx1+mXhYqqPdaqtoaqbKwUaxfi67dYLK0EdemFrrUW4gioLFPOspyB7Hitkd5e0e6YpqBWFKgI9gK9vlpmh0Py18/zWfrKnI3kBx98sBfjsFimhlVNESCRMF2bEQViIOjFrlfITkJRqNq8uHNfAMeR1GwiBuCIEIitEbF0C7NSH+W0BRGQqqO6PuA40sO4NgG+/ZcAHDgn/5lILDYmwLt2IL7iEmDtOmDFSsnCcRxg9aHAH++S443tk9/I6kNSQSrojB1lEMf1dEzKYOQ4kj6fntTdURSmbZN0uyTXR5KS7BdTpdd8LVwUghuTQH0cHIag8oAyVgHmWF6zX0wN1fKA9GzuRBe/MyIHvGqDvIY4TqPi5Mhralevl7mudFGTaLEsJTLXpNLiKjNgBsb2InGexZFcn2KjLhlQAouFtF1Tv8hfs/b3tSUK0zZVSYq4B7mex3KNjEKRqPDstdBiAXpgJB922GG9GIfFMjWNybSfbxxJHVEUZvsgxqFMnDoVk1X/QI0XzV7YgllSp0zqoaRaWSzd0KyBdTpw2ARVhoE4Ao/uBIDUgGUW23bbg/JdHzleIsMAUCrC+YtXIv7Rt2RRs2Y9nJNfBN7+ABiqphgA1h0O55hniQE4vkd+C15Ros2Om4rcOLo8BqrHsNq/UEnbKdUnZOFEjkR09c9HL7iI1G9yAPpgrM/pOHD8AjhoSGRXvxfkqAWZSn/e9xh4xcFpenSeQknGxlOL05DrA4Mru/9M2l1XLJYDheZk6iQKm0B12X4dzswxMhW1qrQqPUkcgF5BtYXqs9HnFeR6FUXitNyfDghmYGJvejspq2kguaCHzfTaHsfZ2mtLe6xw15LHFl9ZFgdmaxqdTtmuXY1+Ln8bkEmAARSnF5Kb8rjtxmSxdILFU8+qlybp6G4cg4NG2spTbUeOAzgO3LP+RlmvnDxPXgEoV+D+zXsQffvf4P7ZX4OWrwUqQ+DHHgFGdoKe8HQ4x50sBy1W2i92SoNpqxTHUws4s1Y3AkgZyTqNOfMbjLPbG78PjiMQzLo7TpWm5Q0AzP8dB1ysiNHsdIgm5wVzegVH7W9bLIuNTBuiKQxCvR1Y/ebc1v33F/nXkIwVra+LSIzi2qgIYQ2uyGa9xBHgl2c33/cC3Zt9IZAoahtGls7iSVAK3Xp7i8UyNyN58+bN+OMf/4jR0VEMDw/j8MMPx9FHH92rsVksKV4hFeXxi/K/67UK8fjGIrtdOXwUAOjRxOUVe3Mcy9IljoCRnWn6ny9ZDhzUQaH08uQ4ku+w6u3JhTIQqzRqInDM6dqwPiEphRzBPfeDoGWrAQBULMM97ZXdjytvdIZBtp7X9bPbttufjefM7YMmeGwvaHC53Ddtb68A0nVVrBbpMYN8F7w/Ii1eMb2u2N+zZbHCDNTHxHlFTmfl5LApzjHdMslUfd/f6bXMkvkVR0qxuqIceUbqdKEsawFNqdqa/eE4izAiPt9QaiCbjhBz/WQ6Qn17LeyK6RxS83E+S1+ZsZEchiE+9alP4eKLL8ajjz7a8vxBBx2Ed77znXjb294G3/fbHMFimQV+MV2I68U0ORIR0wv7vKe0nUrtbJVrdVqpnlD8kp1ILNPCKp2XIGJZPLoTHLNEfsNamsYcqnRkZlBDIs6JEcsx4ofvAQZWgErltM64hyIe5HoSzVULVEqMYBhRHXMH0k9mDHwZdwm89zFwFMJZvg5w9TGc1jled0MgAu0PVe121xWLZbERNlOVel3zz7E4gFw/FazS9cdEABxlKGsBv/0sKhWH2SyxsJlqHugxB/WskWzpDr020qKFMD5n/d0wI8v2WmixAJihkTw+Po4Xv/jFuO666zKq1brvJgBs374d73vf+/CjH/0IP/rRjzA4aGs2LV0QRzIBktO55rDdhZuos+FLJEa0rlFynGzEayYQyWQShTJG2ybG0g1hUyqF9fVyQKUETozIfRWN5XoNKJXFOHVcAIx4XInSMANhAFq+Wm7rSFAvU/mIQMWqqvGn1DgGJB2bVXRHi7wA2UWVkYpN5ADHnATsfQwcxyDXha4nZjjGdpT8HqlQ3n8LdLsgtCx28l1E9HwKyPwXNJRgX5zdXgt2Gb/F/Ua+3rJdyq+tyZwdulVVGMj1Lo6SNoGZtYyNVFosGWZ0xXnzm9+MX/7yly19jdsZzNdffz3e/OY3926klqWLThULmzKxq9rNnuA4IvpTKLUqX88UcuQY1kC2dIvrg8OmWpQ0Ab/UahB6HjCyB6hNykLVKwH1uixsgybQbIA2Pkk9V5A6vF4bdnEMTI5IRJjD7HM6wpuPJnMEeaBNXYPjivp7HCdGPuIIpP+gd+VUFM/W+FsssyOfhdFyfWgjPAlWUcQFYng6rghsucpwyxvJOgXbMjvIUdlvZGQaNFTJSySijM162i3EMj1KP6Svf5a+0vU7/vvf/x5f/epXE+N448aNuPjii3Hrrbfi3nvvxa233oqPf/zj2LhxIwAxnL/+9a/jd7/73bwM3LLEMC/KVjTCslQoD6qFHQHlIVBQB2pjEgXWLcscF84Rx4JWrhNDmWMxKAEQCOQXAM8HqV7H5LjAwPJUlbYXZASscgsk03glGBO2aq/kuNJiJdmdgX07gCgE+X6qMOv5qQiYTuFO/pw0ymWxWGaG2f7QK6RK9oljt02UuFBRmSmT4pg2O0HsL1wvNeQA4/qgDOiFYtAvZvLOyDiSOSmoy19jIs1CsFgOcLoOiV1++eXJ7ec+97m46qqrMDAwkNnmxBNPxBve8AaceeaZuP766wEAX/nKV/Av//IvvRmtZWmiBY30hVm3cLJYFjnEcXaRqhxARASsPEQit7o1BwBUByQbpzokPUCHVoIcF8wsGTylKuA4II7BTg/TIx21OA0arQtRx5OF1VSR3rz6fBS2poObKrX6/kIRDbJYFjPt1N9NZXtmuQaZ2gF5Q0gLCO6v32Imo8RUsVa1tI1xqwXSC1wfoEarcJtJFLY+ZmnFCnctebo2km+++WYAgO/7+OpXv9piIGuq1Sq+8pWvYNOmTQjDEDfddFNvRmpZ2hTKqQfZXggsSwQOGiAjMpusQV0PGFfGcUYQrgx2PVAcgr2iEuhyQZ4PmG2VyAH1Mu1ft1PhWKJKSnk7GXBpAEnKZibTQ6l7memejiuOAbO9jD6HsUvmcR2RtlgsM4dZ9ASiQIygouoLHAWAFgIslJXx08EQdrz9O/dGgeFsY4mG+4VsmnjQsEbyXDGv56KYYfyv8KzorsUCzCDd+v777wcR4aSTTsKGDRum3Pawww7Ds5/9bDAzHnjggTkP0nKA0Mmr2Qlm8YbXxnubemqx9Ard5klFgqP//i8EV38fHKX1vDwxiXhyTB5zvVTQimNZuniSsswgMMepBkSva+O1aJhXgBbaAlhSpInS+uLsTvKfYTgTkQiUFcpgwxpmcsFhE1wfB2cWxKpWzjrHLJbZEQVplDgKxLCcHFVCfEo3IKhnBfk0XkFSmXtZ7xtH0q6uMdl9GUXLtYXVmiDngLPMHe2Y1P9XBuU7UCiLAW3b4VksAGYQSd63bx8A4Kijjupq+6OOOgrXXXddsp/F0nOiUIQmAKCpolazMRyY0xYaftGKI1h6h+uCa2NiaEYhwquuAD90P7znPA88NAwwgx/cDFQrwMGHAzCEED0frBaYyWNje4AoBA+tAhXmqYbXKygl9yCpmQaz1K2Z0Qbdvkk5qzhoAiO7gKAOWnWwKGbLBgAYFDZUS6wICBvg6nJlQqvotevZmkOLZVbkDN+ko4PR1knX9uY1P/RvvJc0JlMnGHNrL+N2tIiPqfvFirTIY9go8nxBjkTtLTODnP7OWXZ+7DtdWxTNZhNEhHK5u7YjpVIp2c9imRemEhiaCc26iJcA4m2vLrNRLUtvKFbBk2PgrQ8iuuVXQG0SzlFHo/mv/wL/vHcCO7Yg+s/vgA49SkS6DjoMKJbSejHdroMZqI2Dtz8gol6Dy+dX6MortOlHavzelCo1q5ZUvP0BEeo68qnA5Ggi8JjPrM78rvK/MeaWtb7FYmmDrhnVRrAWvGrWkfmdxpGI6hFSjQDTgPVL0xvIcZyUfWT0BFRP9YxTWV+rzLk4Py932tf1ZDxhU267vto2lNdgndcWi6XP2F42lsWL5wOh8ow73uz7PMaGSIVOMbWrdUsP4D2PI/ryv4Dvvhv01Gei+JHPgYgQ/v63qL39b4BiEeWPfg7O8lVpuiSzpCnreuAoAPklcHUYKA+CKoMgr9DfqAqRpOM1VR9lx5V0cNeRiPfAcmDvDnCzDnI9iZ6XBjKGMOuINCCtsLQjAFALebsItlimxXTqekWJ0saxqj3OOY5dH0AsD2vDWhut3VxDwkDUjnUUWkeEGxNpRLqo2jYFzXRcuqc6kBXwY87tW81mf/nFdEw6e0WPtzRg2y9aFhbtyhfm+3yWvmKvOJbFC5G02JmrIqdXSNPTXN8u1i09YeyGG/HQuecibjRw0NMOxapnPDd5zjvuqfj/fv84Tv2fp8FZs17q9zRRBCqWwGEA7NoGWnOoPE4E2vgkUHlw/2Q6FAxlWSLpq6yg5WvAk6OAVwSP7RIDOGwCaw5PFrai2r1cbusd9W/XZm5YLN1hqlKHTSD0RQQwb0AWq5KmbG5rEjZbFegzzzdUTTOUvRulytNmynYYqNKnGhLhTY5lbs7rjOgosjmGToZvHGWj0FFgjWSLxdJX7BXHsvjJL7CjUC0kSBYB06Vp+UWZfOPYTsKWnrHjXz6GuN4AmPHY77dh5eQIHCIwM8Yf2YKdj49g2283Y1MYilq1WhAyIGJ0jgMMrwaHobR90mJaE5EyWEv7wbjUKrqh+QjgFUFPfAYwMQpUlkvKeBSAJvchMYn9IiiO5HdWKEkGR6yiyYVSm/Rui8XSgusZNceOiHSZWRpEkpkRBWn7JCBNq470/WnmOq2KDUB+5Ub9panI77hAfQySYWI836ipNO9K9nFTSXmq+TY/b083XsvMiALJStCCjWEgn6njSHaAFUmbHsfpbxmALTnoOzO+6nzlK1/BVVddNe12e/bsSW4fccQR025PRFYJ29IbmpPpBN6sTS8awiyL/nzdlcUyB7zVq5MoafkJRwBrD0K8ezvguCgEIyDPxZrXnQ1+5G5g+RowEag8lEZ/It1jWS1WVB2wLG7YUKLuI826atWSS+uMI2B8j6R/eh6oWTMWWfq3qFK1AVnYu6rlDEdWuMti6Radoqz0ABLjNI6AmIFSRc1navtCSbbR14pQ/S6nu3aYhjA52fKJ4gAQNSUFW8+ZOkps/q4Z8rsvDaTHLBn7TmUkkyPR6DBQ1zrblqhnMOeyl4ySs1hdj0vt27xaLAcSMzaSx8bGMDY21tW2Wrzl4YcfnnI7Zk62tVjmDHe8056gYRgmAVDaT+msliXFIRd+BI9eOIhoYgIbzjlTFKAn5bro+i6e//kLMHzIcmBstxiYazcClSFJ+df9TEEg7T2O5lGoq2uMaJUW7gIAxLK40nXVs/F4WykAi6U7yAUQG/OUigYWS4YDy4j0mnod3WoZlAaUEY5sNBhIa5R1JK08JBkmjitRyNp4+2MCap9Sd2NwXKBgI5oWi2X/MCMjmfPRA4tlIVIoy+ROkJTU6WCjRsp+xy09wl+zBod85B/B2zcDj20BJscA1wd7HpyhlVh39EZgQup6ad0Rqm2S1PCx9ua3a8uhI8izFaqbC34pzbrQv5U4ktRKv5g6Oxm5qJKKisexkW6te0WTbb1msXSDjh43a2mabCKAFxs1yIZwVn1cHL8zLSVy3PbRxDiWY3KcCvp5vkR9dU3yTOdgS39JhBiVE0S1KEyes59Zd/RbT8MGb/pO11fNf//3f5/PcVgsvcPzZ5aa5RXSCcL17YXI0hN45xbw766XhW11SNIGR3eBDj4S5Drglesk4hJHwPK1me8dOS5QXZ4V3nG9tIfx/sJxgcpwer9ZA3b8EfDEGGa/JIYys4x1cKX9PVksvYBZjFNdj6zTm0uDUhNs/s48P3vtiMLe6G3EMVAbRWKAMyR7RPU+ByDp4DOdgy39xy/Y3sgWyzR0fdU8++yz53McFsv+w/UNlexFFM3Si6ZmTWrRXFdF+prymnIteCzzSGMCXBsHP/oQ+Jb/D9se3I2Bwzdg6NB1YjROjoM2bAJWHaLErMQQ5vVHyudUrMji10yf9ArZhS6wf6LHU0CFMviQo6X9i+NkPeuON//fv8akiPQ5nlLTXQLf92YdXB8Tp0p9HByE4D/cAfhFOCe+EDSwLLt9FAHNCTFYiuUF9x1ZcLASn4tDqaEv9CBqFjbTqJwWjJzKSIyjVDk6EeJS/Y61QVsoZ7/PcdSqUO36QFBLX5fe3vPVtl0IZM0E85gax80qbgcNK4BpOTAgp79rxsW0Pl0i2HfcYgHk4rPYRLvCphjJUQQglv9DUVNG2JTFimX+iUKgMQlyHND6w0Envwirl7lo7NwFjlXdbqkCOuxJIDMKC4CWrQWtPhRUGhBj2Veq1eVBlcI4JIvlQlke62dv5C4hxwUVyyC/KP2bXb8/GRlRqET6YnEM1UalLdVi/t6zitTFSkSwPAD+7S8lLX/fTsT/fX3rPs1J+e3HofSVzRtSlixhIxWfC+rZlkQ6ndns5Tsd2ujWwnrgrHhkO5o1VbIQy/eVlXJ9FMj+UZAVUwJaF8heUa4ZeRXi0mDq+C1UZpdq3Yl8SUShogTAqPM2FovFskix7j6LZRHCzMDkqKgN+yWQ9TDuP4zFLBEB6zageOarsfrXN2DH5m0orliGVf/zpSBl4PLy9eLcKFayRnM7A9j1bFSmW7RxHDQkzXuxtzAhAuAiNUAYCMPO22ujrDYGVJalqugHMtro1XWW+Z69gPx+HVccE5MjYhw7jkTySwPT//6CZjaKO9txTre/FsgKGmnWkK4thSpxKJTS773j9v434PpyvjiUNlM6Wl6qyrjIWZCOPIvFYpkNdvVlsSxGJvYCE6rNWqkiixfmVMjF9e1ipU9wHIOiyDBKCHTQYfBefDgOIUIQEaic1hFTeVCJ3FjmhOvJgj2oJ0rbpPpQkzZ8FhukDKHaqNyvT4Ce8DTwjoell/YRT27dp1iR7c3AJUcQA/sAZ3xvWrJQGpDfnaNajbFyODRUK5zJUTH+dNo6kTxXGkj7DSthvYSgLtdirwDpI6yeK1SmNnoLZZXyrfqGgwHHl+90HMoY2xnn7Wp9ieQ70A9IiewhN7doVWuL5UCCCHCscNdSxhrJFstiRC/sAFlQAXIB1YrCth65b3BtHKiNgirDYLWw1SrPzAy/aJ0V80axAhQr4Il9SURfDOVF3E2qUJJ69bFdABi06hA46w6X7JEwaN3ecYHysERBOVbZB7YuGUC2pj9oyHtVV+2JTCM0CuQLUyi1pklHoRwnjgCQMrRdyeKZFHV6MWxdMbArw9O//zrKGwWyyGaI8WkdmxaLxbJgsEayxbIYKQ2mwi/aMAbSemSOVS/NHEFDoh/kiArprPrZqpq7SEU8dCsfZrlfqoqBrmp1AShBmmB2523W0pq/OBQHgK4XZFW/53qSYtqNYyCOwJOjSjxnAjyyCzS8Wha/YVP6GU+Mg7ffLxGlZh144G5ws45wzzi8Zz4XtGwZ8PAfEP3hdxi7/rcYftfb4axYKccZXp2MgwCr8toPjJR3juNsW7duadaVCJg7+99Gr/B88O6dQNEHFYwIXScjynGA6rI0bdc6yAS/lIpKFUrp9QjIpji7PuCaDohc0+6kPlldXwtluQaSMnDjWP4fXDm78gj7efUPZqAxblwzDMdHP2g3B8exKJRzLGnskdIVKA3I+BqTkDlWOcFiVc+uX49uvaU7C8TqtRXKcg7L/GCFu5Y81ki2WBYhVF0G9ouyeCtUJLKsF4NE7S+mWqgGUIIx9elT5OJYFoOx6onpFVIjHEgn42T7UBYByYJU1/0F6Xkb4wAcMR6ni5zEcXZhS44sbvRigFmcAbqv8HSvJwpVuxSAXBdcHACtrQKOJ0tivwgOA5DvgY56qor2RMDy9Yi33ovi858OdhxgdDdwxLFw1h6BwWeeCcQxuB4BaytwdMovqcWXZ9tszDuFErDvMflsoggYWtX9vrpuVUcd40i+w8Xy/Iy1S2jZKsRXXQp63ivAZckMoamUmK1x3Ep1mfpcVZpwFKbCZuQoh54SbQxqqeHsFcVwbmc46cdc5SB0XOWkWD4zQyvfpslqD/SHsJkTRWNxkBVK8j+R3J7OIOFY+sNzLMao/vyChnyujgdAZXf5BZkH8nNwY0K20QYwkP1ONCbVODg1hlsE3ZSjxvzt6++h0ixZlKUnFssCwF6VLZZFChWMRbwWmImjVoGajgeYZps4ktREjiGTvfZQ5xbjZkSGSLY3o8v5c8YxgBhoqhTFdotDZlksaKGi/HHM+wQA7tRqsnqf+hhYRYgYaBU3YpbFKw2kPSSJgMoAnIMPB+JIL2mAkZ1wjnoSXFepojebiLdvBQ4fBnmeLLTswrcvUGkAPKwWoKVBULfvu1YmjkxF6IWRq02r1sM5/ZXgbQ+ADjsatGyVjQrNFC3YpakMpU63YiVrCJUG5ZpDjhjJ5jWupLJMdK9y/ZhOsS6UZ5554PryeUahXHOsITP/6N7SefRcp6lHnbNJotD4U9eNWijfAUCcyno7/f1qhOK84zg7T3GUKqLr8WkHsP7+dauyrtFGM5BqlFh9gvmh345J6wTtO3YFZ7EsBYjSSXrKbSpqIZhbPOZhFpVcc/IGA0zK8a0fVwZ0frKojaf75o9rEoftDcnGRNqmhZRZGnOqQuso7zoREotmWs8/Z0VkzYgvsxotJ7WBxAxwlCReirNe7VyqgkEgzwM5joyzWIR77PH9N4zNhd8BbJTPWBBN9xnPL0Idb+rfRh+hDU8AbXjC/h7G0oEclcLaBsdVStFtaKcyTzR3p4VXsJkm/SCO5M/MJABSsUtzrlLXfdRGpb7cnNeiMGtM6/nMcdIIsDZy8542bTznxd/0HBZH6eM6I0ZHkfWxtNgCOW2MbUMFP+/Itlgss+LAXVFZLAci7dRR26Ejwe0wvd1pXNWg3WPm02YUusNErtOzk20dwDEsXO2RNxeu00VyiEDKqGdyQNpA1k8zZxYwnPQ9pcQ3oA1r+EU4xYpxznjqc88nzUlJqwMkOjads8SSOoE497k5voow2gWmxbIo0caj/g1HATCuOkF4BWV4Urqd6TltceJG2Tkm71DT81AmC4BkXuCc4W2eQxvG5mXGdDTruc2MCpvHz7cM0/NQrKLVWjiOGYhiG0i2WGaJrQK3WCyt5HtslqqqxsqYnB1HTc6UXRC4fveph9TBmI5zxotO4zYXNk4uxdqZ2ufHYHAYJJFXzqe+5Q2jZk1qj0d3pXVgzEAci3Gd2Z5y//eRplHDpo1ly9ToxWQCyfeHI6mZn2mKo8Vi2f8EDWByn/xpR2tgXB/N3zXH7S/XGUM5t4HrZR/T25rzlTkveAWJRpsQUgV0cz5LnLK5Y2Scym2c15m5V2U8mMfZnwKESx0t3NXPP0tfsZFki8XSCpHU3MUhQEqYxvWlBitviOUnYY5UDV+gUttY9o3DVtGRTkal66saQNfINiNZ8JCTRsOTiLaql87XGOfPFUfg2BGhLp2yxjE4ikHGvsyxpF0Pr4aOjHMi6mOkR6rHWN/mCNRvt71WDte3LdOT/876xTQNUyvE24i8xbK4aBoij80aUPaz7bjiGCgWgPqESlt2DSOUUyE2AElE2MRxgcqgEopsAGim+2rRyPoEEkPW9dM5Q6djO15qvMaGlodflPktVv243fy8qhzDZKSHm/XMZDhqHWpvcFsslhnR1YrqK1/5CgDgmGOOwYknnjivA7JYLAsEouwCg0iEtPKpXu32IxKDWrfiiQKpBWzWUoMO6CxWUx4UEaagKfkucaxqBkuq9ZTab6pIcMuwCDy8Jq0pU3XIBAJcRwxjEEiqjZMFEjNLwNuRFDnOnYuTxch+8vTq90or+FqmhxzV8itIxePMWkW7sLRYFh/kpBki+lrsF6U9YBSI1oDryXNBPbtfsSyGr64N7iSAqQ3YkqecuDHgK+G2fJmSHkuxkjp89TXa9bJK1tpIB0mGVRRlnb6JwW4ayDm0ynUmw8pGH+cNRzkk+nk+S1/pykg+55xzQER485vf3GIkv+51rwMAPO95z8OrX/3q3o/QYrEsDFTUNbmtyaeXmfVQ6c5IxMVCV/U29qeOfHKctpjStcKMVsMdANxi9vEO46emiiAkY1NGr6oPI1NwJdkm3bZlAZIMw0hB7zfdiLZZWnFcoGAsQosV5ZRR6sYWi2VxUayqtkm5a6JrRG8BAE5WW8OMKrPqed2YBNyhzg4z7UTmOFtLnMGI5ubFAPMdIqJQxqdrpmUjZIziTBcJI4qcPBZPKQdisVhmxpxz8y6//HIQEQYGBqyRbLEsNJJewkYNre55nJ+kwan3Pb9frHo66gVBu1Qur5D1vhfKabsVvWDRqWc6Da0TWozKVAJtaZXhyD11XprqeOZrApJINMVxagKr53RaNek4s45ax7FElXOHJZVuTX4XvTUtM6Pd93e+cP3pHS0Wi2XhEtQle4khWSIFVylSj8nzREChCkzskRZ/pkHZnFRtmwwDlGOV3tzuXA3JjAIkhbo80JpNNJVOhlkfrc+ZFwDTIlw6+0pfC10fiHNlT0kUOm98x4Bn5yWLZTZ0ZSQ7jgNmRhRZMROLZdHAnLZSAkl6abOmhExIeobq3so6Bdn1JR2sNqoUNn1pi1IbyU3g2ngxHmvW5a8yrNQ94zTyHCuFTVNV2HGkdrmd8RM00bFeOXl5MShmcG0MiELwqg2pQdsOgiyCtGCKjixMjgNlJUwWS49nro2BKgPZsREBUQB2/aRtlKmgyo0asGzt9Ma6pTt0iyZWNXqlgfk3lC0Wy+LFLOWJmgBK2ceY0xRn5lSF2vOzAl9mfW8nzPKMOMxmWpmPd8qWcn2AGmjpnZwOVs4fZ3U8WBnXLSPLt4VKXocNLc8btk/ykqcr99LQ0BAAYNu2bfM6GIvF0kM4NhYIyuCIQiS9F+vjwORotu9jFACTI+lkGwViIOcVrHW7JHOC14axXoSYAl/Nmhy3Npo1nM2Fhonno2VyJ8p66ZWiKBXKyrAfm+YNkZriTD9KZlCxlC44HBW5dh21GMmOgVy//bKJXHktLcJkllkTBUZ6f2zfW4vFMjWmQaqzQjJGqiG8qB2qRMopO0PyBrE2us372okchcDEPmB8r0S4k+HoGuoOhmzOKGJmmUubDTlmpKLHelbKG1FtjW+LxdItXRnJmzZtAjPjmmuuwR//+Mf5HpPFYukF7SZMsy6r3eSs2iOlrSmMYyVRUyWcZRqtmfYVTufz5yftTpN4sQouVsEsqtKZ6mZywGpRkwzPL3VOi9OQRM8ZDI4icCx/WsBLn4uIQEZdNceRpFqr82kla/k/log2qUi99fT2jnzqun1vLRbLVBQHJPOpqDKDGpOq13FR7hfLYiQPLM9c8815UKYpbhFobEFHjk1HcRSJczgKxRjW0en6RNp2rjYm42pMpllehuuVzcMb859ochFIZ9Qwsg4APb+GTdVZIkDSj9kyP9gWUEuertKtzzjjDNx2222o1Wp40pOehOc85zk46KCD4Bhes5///OeJiNdsICJceumls97fYrHkIJXOHDZSYRCN2UtRk4kUxxAfWpsJtmU/UYUGR2l7i7hTChnS+qspFiAMSNS5Pi4iSsUyOJkgWLWmorSfMxFoOmVnZvD4HpX67clixiuAtSopERAGiAE4hZJanHA2Lc8cH4BMhTIRyCqJ9g7Xk1p2nSJv21tZLJZuYJYaYyIgQmrIxhHg+HLdrk+mSteeeW1R1/z6BLg02Ll8htvc0XMLkCpfA7m+66yyZFrnR9NATvZTjtxkfss7u1sOoiPc1ji2WOZKV6uOt771rfjiF7+I7du3o9Fo4Jprrsk8z8zYvHkzNm/ePKfBWCPZYukxjqtUNUlETeIQgNNS55SgJ/MkctxGCCSOgMhJW15oMa8ktSwCGjVkVhH5hcZ0UcGRx6WeujIswliJaqd476lYbdPDcmoDlZmBWKK+HIVAbRzxzq1wNh4rwl8qhZyUA4GS16+HLDdYCZ+R8TqY2Qp3zQdeIduX2mKxWDrRGM+VZRiijzrqG9SB2hjIccBEIC+7DNbXdS6UlFHd4ZpOAOKcs9fMuMqnX08DgwytC/VYpBzQzRrY89VcyAAbIpxOLqNLK2I7rkSTrVD//GFbQC15uvoFr1ixAtdffz1OP/30NP0jSVMR8o/P9M9isfQYLdxVHxd1T78IlIem2UeJJCVe6FyKtFbWJCgDGakqsBYJCevoWGPVVlikDY0JQ2mYQCpCTaTUpvViwWS6mtU4Am+7P7kd79oO96jjVWqeK5Fqz5vSAc9xDEzsa1W4BiTNzV7LLBaLZf/QzkAGsvXDUSjPuB7I81vXsYDKCnK7F2FMMqPyJUXaw6pSZXWZkjpHWtaUHUOC60nKdiWdt0mXOZEy0Nh4fUSyj87ocmyKrsUyF7rOXzv88MPxs5/9DDt27MCdd96JvXv3otls4nWvex2ICKeffjr+6q/+aj7HarFYZgLH2TSvsCnpq54PNMNWA7VQBtxBiQJHppCJ9k7D8HK3SfdKJv1pFhZmqnWnFNryoIiKFSvZRQUkqtxSadVOWTSP64HHRxHd+lMgDkFHPBWII1AcIY5jOOVqNpKuFkzmeYgIPLC8/fHjEFO2DLFYLBbL/GG2S/KL8heFQE09Ro7Mc81aMvekGUIqtVk7Z6er5/UKktKtWzdpA9U0fnUWTKZtoiPzWxSJ81pnSHll8L5HxWnry34UR2CvICVTRjkRkQNAG8bJPzL/hIGxrZ4XraFsscyGGRd5rVu3DmeccUZyX9chH3300Tj77LN7NzKLxTI38h5kreapJ9DAUJ/2S+njOn1a92nM1d2CYYh/5T3hbRYVnfoqAypdrI1RObhKBFia9VRMC2o9QQ5YktOy55jG609EcJ5+OjC6S+qn4wgchSDXA3lu4sEnQIlxOeo2gzhOF0JqOwaS3sqZdHPL4iGOgUD1OvXLM0qPtFgsCwyvKManjrY2JiWqWhmW37rny+MDK4HmROqwJQdwJdJLwPR1v8k2LsQQhYhzITfPxSEAX+aO2DCSyQEQGnM0pU5Wh8Cq64O0NGTALajZTh3XARCaqd5qDPo1Jtj03Hkl33GjH+ez9JWeKKHYdGmLZQFCJL1lw6ZM0q4xefpFWQBEqo+j+VzSlkLfnlpkK/s8Tz1p5I/FEQC/ZTMC1KIhkkWP5wOmWdzO6J6qR7LeZ2Kv8vgXVcp4DCYC3EISBefESWCMxTgGZR5XseZksWUnsUVFczLNHuBJ+b1YLJbFSW0MiaGqs57iCOCC9n4q328owl1+EaRSpEWLwshGinX3hymu6Wa0Wet8mE7hjGCXRo8vO88kEeEoymxKupTJnFc5P9eQ8WexWHrFnI3ka6+9FgBw8MEHz3kwFoulxziupJe1Q9cStzzuKuNVoVtL5AW8zP8B5Y1XlxQt6NWyTV7AS/dWVgax44FdV/pJxmE65WuFY3M/UpFu5QDgIAAmRqR+qz4BbtbBQYDGg1vhDQ/C8T24y4dkMeT5adW0Wnwk9WfkgKMoK5JBpERcDEOclUqpVl5eSMJdujbPrIE7ENGfkfndzD/f7rbFYllc5Fsampe9UGVNBXWwXxZhyPJAOo9EYbqvnleAjIhW9lyMpP7YFOpKjGZD7ZpVNwZ9LFbbRtnyIAKBdZp1coTc8fXrdN32WViJpoilL/TbMX4gz+X7iTkbyaeeemovxmGxWBYKXlHqmpJFAIyJWnm/y4OyTXMSgIpY542QKJB0t05oIZOJESQtOoKGWgR42XZKfkmO35hMz8MsqXTlQcQjO4F7bgI94U9AzUkwx4h/fzO2/8vn0bj/YcB14A8N4LAffFdqvBL1anltolitD8sSaW/WVIspJWSWX6zoftKFMlAozf797jXNuvpcIJ9lqbp/x7M/mdgnmRSApFzmPye/lL5X/gL6DC0WS3doPYraaDaK2ykDKpPKrMppdBQ52Z5UtlWb7CRmiVjr2mc42bnBLFeKI2B8T1q3zAAQS0ZTFErNsh5zHMkU5LjpbdeT8ao5C0E9EZpswXGAyDr6LJZeMu+NJ8fHxzE2NobBwUEMDNhUNotlweM4YvTWjdS1/MRMBBTctI653WLE9WW/+njrNq4volzaKE6OWUoXBxwrsbGKGOWxai0Vx0q1UxYyPLYH2LIZdPQz016SjgNn1UE4+E1/icn7HkIwMonKn/0v0MBycBiYo5R067Hd4IFlslgiAhXKsgiKDW+/V5D3YHLUGG954bUoMmvNwyaAA9BIZgYmR1IDGRCnR95I9nzAVcqx1ktvsSwuGrXUUaujrtro1E6v2mgqqKWFuyZHwGEA8lQqc6Ei1wotWFmsTOE0Y8NAhsxTXkmixWGQGshkPA+kGS2OLmdiEeRyXMBR82FjQh3PT8cRheLsA2RergylWgqmI8Avyu1mXeqrAXVsq5VhscyWnhvJDz/8MC655BJce+21uPPOO9FspouUQqGA448/Hqeddhre8IY34NBDD+316S0WSy9wHGkXFUfpxN6O6QwL11OTuqRSJxFjPXHr+1phFEgiyAyS/sW6TtRxgYFlkpqtRVUcF9izDbTyoIyxSkTAuo2glQdh4BkO2CvCKUnaOXk+wrFxeCtXAyCQ64LVIok9X9pNOQ5QXZ4uhsz2HQOeLFKIJO1toaW36eiDvn2gEUcS6cnXA3ZyZljj2GJZfDCnWSD5loOlATEax3bJ/SiUa/rAMulQsGI9EDTBrisRZceVa2WsDM2pDMukk4IyyGOW44dNZQAzQLlaZj3HaaVp0/kaR0BlmZx/2VpxHDtutkTEL8ox9DXMcYDqsta50PXTbfMtpyy9JxFh6+P5LH2lZyuoZrOJ973vffjc5z6HWF0A8oJejUYDt956K2699VZcdNFFeMtb3oKLLroIhcICi8RYLBYkLS3mfBwn9WznFaAdTwlzZdtx6PPr/siZY/m564XrAUUxgJNrTtCQ/YuVdMEhGwBE8JatyBrVnpNTBZXztzWsXA9YyM75YlVF4pFpG3JAEIUSQdaLWJ2RUKhIVMZisSwhjPpfR/W49wpGhpPZKilNiybHTeaM9FBdznfkAIESw0yEvZSOR6SynBo1cQ4zp9FpZjl+oSxK2FqoKzZUrnWkO0+7eajdXEjUXmfEYrHMip4YybVaDc9//vNx8803T6t0rZ+Pogif/vSncfvtt+Oaa65BqWTrwSyWAw7HATwvWWq0lJF105KnUEl6Q5Ly7HMUgjILC07rjpsNUHUJG49EB2Z9LbMIviUCPOqf8uCB+X5YLEsZIqAyKAYpkURyidIeyK4v2VC63KeXyvVayBFIDWtVX8zMQKkCikMAJMZ7HAFgGVOhLEZ2YtwvZI+rZUocygp89uN8lr7Sk9j9eeedh5tuuim5f9xxx+HjH/84brnlFuzYsQNjY2PYsWMHbrnlFnziE5/Ak5/8ZABiMN98880477zzejEMi8XSS5hTdc75IhQVZlPNkxt1MWgbNbB6ftpxshyLowgcNgBQxmFHWggsDICJfWDb03jpkVe3Zai0S2sgWyxLEteXiK2ux9VoA9bUJDBvz4UOcyIziwgYtWlV6LhiLGvHbaIPRv1XSLZYLF0zZyP5tttuwze+8Q0QERzHwSc+8QncddddeOc734lnPOMZWLNmDarVKtasWYNnPOMZeMc73oHf/OY3+OQnPwnXdcHM+MY3voHbb7+9F6/HYrH0AmYREamNilBV236PPTlRVo20PgFETTFoPV/OP90R6pMQ43oC/PhWIAqlD6Y6LpvRaK0S2qzNw2ux7FfIyaYlVpcdeOnmFssBCDuuOFYBcByDtXK1YRhzr4xkw6DVhjGqy9WcouacnDik2jg11M3nozbbWhYHuia5n3+WvjLndOuvfvWrye2PfexjeMc73jHtPkSEt73tbWBmvPOd7wQAfOUrX8GJJ5441+FYLJZewHHaaxdqcu/Ub3lO5+H0PLpmSy9CHLerFDn+r5+D63Vg21agNg4+eCPotLMAZxJcLAPOQJrSFkeicGyji0sPImnzNJ3YnMViWVo0asD4HjGO40jmkeqytH0S0LNIMiu1avILIJbSHgBKhXoU7HhAcxK85jAJGNfG0nponRml51eCaHJYLJYFyZxXEb/85S8BAOvXr+/KQDZ529vehoMPPhgAcO211851KBaLpVfk07/my4Op6pF1mpoohkqLDCLK9kruxNAq0MYngk44GTjmKaBT/gzk+0B1WOqUoxDcrANBAxQ2QX4RlFdDtSwNtPiONZAtlgMHz5ffvV9Qt5V4VRyDm3W5/puK0nOBHGk9CAKb2SuqRSARxKFMrvwFzVT9WreDSpzBWlzQzkcWy0JkzpHkbdu2gYjwnOc8Z8b76v2+9a1vYfv27XMdisViyRNHovTsqnqoOJa0MMeVxUQUpgrAserPSKpFhU5N1m2OgrosPhxXTfq6J6SRMq0XDHGUHgswPOfKo67OK7XHE0ChImlrup6LGYhDiQyM7ALveBjxIw+By8vgHX+iKImO7UO07WE4qw8BinJeOugwMYAnRiRa7BvjaVf3xQw0JpWqqIouBw1wFMhzhbKMR6dv62i3jljEUfraC+XFa5xFgYrAFBbva+gGVr1J4zhVhyUn/T5rcZ280rnFYlmYMIM4BpcHgTgSwUbXS+cwnc7seHKtZ5ZocByreUrUsEkJf3HQkDlzcKX0Uc5h6Gmr46rrpSsCXQzIPKbnOc9LFSkdV80XxlHMaSmOVLsqt1VpO47ktURKgDJxXCslfz2o5NCqV/RSvp7vb/pdT25r1/vOnI3kWk1q+wYGZqccqPfTx7FYLD0ijoDR3an3ujIsNb66vrhUlf+bxpQfNGTRENTlPil1zvpo6vkuVkQx1OzBqPsda2VRfaxiFYB6nHOe/ADKIA+B5gRQqCLxrKvJh8BgjoBKFRSMwj3xFFAciEHNIaK77oDzjGdJ78skGq2IAqBUkfow8/FiJXUAjO6SFh0MoDwg5w/qss6II3kfdF2rXsDoxQeQpovr1z64cuFMZLqfp3ZMMKt+1bnemWEzVYClmvTsXCivodfUxrI1gGFDtWRRr58ZwCRQGrT1zBbLYiBsyDVbZ5BEofzGgwbIVdc6Vn3t9bzWrKfX7SgU8a9kzgG4Pg7etRU46umgfBYVAaSvq0lfBohxHjZTx3IcpX2ckxIiR641ei5NjqnGWBtDYu2WBtO5NArFmQy0iTq3i0Kr4wV1qZdeqtdzi2WembORvGrVKmzfvh3333//rPZ/4IEHkuNYLJYeEYXA2O7shNqcVClhTuf0Lj2xJvcZaBgGBAGoj0FWCubES22Oy2pbGMZ0rseT5wN+GQhqstipLkPLpO96oEIReOLTZCEEyUKJXQ+Fl74aaNbAo7tl22JVFhOOK06BoK4WM+qc5UGpc+ZYHAhxJClxvg8ETTBYtnR9kFcAm2NRaXaZ10iGUa+N6IUQhYyNBRc54hCZHE2N5upwGokwjUZtSPeiP/ZCJC+Sw3FWREc7E6LAGskWy2Ig0c6AKtVRApA6Myg3nzCQZkMRSbsmz884EmlgucyVzUabfsqOOEmbNQAEVFSAqFRVdc/Kiap7tavyIYDEIddULaugjGyvmGZXmWONgvQ6PKVwpp5Pc/OmNuIXypy0FHH6rH1hswL6zpxXQscddxy2bduGG264AQ8++CAOP/zwrvd98MEHcf3114OIcNxxx811KBaLBZBFf2NcJsYwSA3TOGwVrGJGe080IN5oTid5nV4MpCna2tDqlMqsj81oNZSZQSDwqg1JhJPUxjw5mtYNxzFQXQaqDIMdR1Lr4ghUGQI5rkSGq8PgyRqw5hBZXDguEm+648qxGEAUgCf2AnEMimPVp7mgUuQcMdRdPxkr5b39yUtTUWvTYHbchaM+GRl9ODmWyImO5GvDUBuBbkGi/oASvFqg7bF0arxePBar3UVI4kgcBvlMBgAAyWdufCcBZFWyLRbLwsX1U+dXHCExGhOHbEuCdFYs0nT4GumzVBkEdzIuK0PibDXTbV0fGFwlTlp9TdGP63M1lAK23o+hto3FWaxhBmrjKiU8TNoZUrvrXXLdMl+nTuV2bBTZYpkDc17RnXnmmQCAKIrwV3/1VxgbG+tqv4mJCbzmNa9BqNT+XvSiF811KBaLBZDUZU2SuqzuJ0adrmFK08XakkywaiL2imJ8x5EYWmbPyKTuql2kmrNGStI/0hOBLlf+l7E64B1/RPT7WxD99gaQ58tzyovKIDmvThsmArk+nGUr5VheAeS4IMcxjiuLBW5OyqslArtuxhgiIpBfAucXTLnaYwak9ow5qT9LD9L5rZw3tBFYG0udGHlDNx8ZNp/3fIm6lwbStMOFSBymi2EthNMN7VL9AeM9UAtpjsXwriyzRrLFsljwi5KaXKzKvOB6ku4cBkChogzRQK4dYSh/jQm5VvpSv5y0clIwIHoZUwlqEeSaO7EvzUbhSIxeoFUHg0wjFmk5jG4NpfeDzHHsuLksH3EOcxwlCtvZ8eRSuJNzWFEwi2W2zNlIPvfcc7Fu3ToAwK233ooTTjgBP/zhDxF3UBJkZlx11VU44YQTcMstt4CIsHbtWrzuda+b61AsFguAZKLUhqXZVgkwIrxTqH06hlGlU4r9okov8lIjwpysk0hxm7G03Iaknrnto5a8extQGwGqlZYFDAjtjZhpUoRZLRiSY+XXEyq5mshpja2bUXAgk6KbHdt+MDBrY2qRZdQWu35ae10akM+uPCiZBOXBNqIwcdbhsSDJvbddv9dttss7EYgk7V6r5FoslsWDFqZMnMJq3vN8wCH5YzXnuSr7qDIk+5ByunIMjqL02t/O6DSpj0s5T9gEJvepVO0p5lTTYPUKxhyr8qfCNG1cTq0e11FklR2VdoHIlzzpnR1l5XNaImWZJ4zPoR9/9sPsO3NeDVSrVVxyySU466yzEMcx7rvvPpx11llYtWoVnvGMZ+Cwww5DtVrFxMQEHnnkEdx2223YuXMnAPnxe56HL33pS6hUKnN+MRaLBeJRb9bUJBmpCGybeuHEWMylpLkFWUSEARDWxXgolLLiRqSM5SSlVxveBCBuY5TDOJfCnapOygGGVsE58umSYusX5ZxBAyiW0qgz1CIiqANuATTVFY0A1CeBgqordn1tFsvT6mbyiLmo0a+PHPHuN8aBQilNuc6k9vWZdhF6IF0waoPQK7R3LoRNeY8BeX8XajTZ9eR7GDbluzfl98egWFblAmGaTp5EaCjzH2qjWcEci8WyeMhft7RSNCDzILnyf1HWmwQ1fxgJU2QeZ6rrYNzmuqsN7rb7GXOJrk2OdZxKTsy1CSV2aZ5HUsjZcaQlIrOUD+nzJHOTm94n/eqsYWWxzIWerARe9KIX4Wtf+xrOO+88jI+Pg5mxc+dO/OQnP2nZ1oy6DAwM4Etf+lKSsm2xWHqA60nEUAtuaczoWZJqbaDraQslmWx1z8nkuAVV66qPp9U9dV1XmzRrvRjIC3YBYnSXB9Cujtc57FgR44oigGNQHAG+JwaPYRwTAIoCsKfHVm45VvLyyJE2IRN7Aa8gaqTyhDpeDIrZ6M2sFhsxq9eqDHNX9eHMv9ZETbrPNb3FaurAUIu/JAUbkPe3PNh5wReZojALVLhLv9d+qbWufjrIkWi6RjsE5MDtv5elgYX3HlgslhlAqSK0qTNh6mOkW6aP6YwajtEyR5qUqsCE0vzQ9cmuLw7doNExSyqJIOcjwTqLhc05iOR4e7YDAytyAlxq3m0r5mQce0FnBy1yyGm7fpnX81n6Ss/e8Ve84hX4zW9+g7/+679GsSiCMDpN0vwDgGKxiNe+9rX4zW9+g7/8y7/s1RAsFgsgIk2TI9nJUadfmUwl0sQsC4zJfRJdq4+LEZpJyZ1BmnHbVGzOKpOalKrAwDI11ztZgyXzuqQXJrXrK9luGH5RlJ0LRak5yzypRE7iKI3OmnVj5musDLf65/eXcJdflAXUwIo0UpyrZev4PgPZhZezAIW7mjVxbEzsm/p1dCIKgLFdwOhOUXw3o8g6NbHdPhaLZfGSqQc2rss5HQ0yr+2xvt7H2SycdmihrqHVqXOSSPQdhlYDRcMxlwhrkWS0AADnDXUCLVuXncdcF1QaANYdiaRdoumUzhjC1F57wWKxzJqeusqPOOIIfPnLX8anP/1p3HTTTbjzzjuxc+dOjI+PY2BgAKtXr8bxxx+PZz/72RgeHu7lqXvC448/jttuuw233XYbbr/9dtx+++3YvVtay5x99tm4/PLLZ3S8q6++Gpdccgluv/127Ny5E6tXr8aJJ56IN7zhDTjjjDO6OkYYhvjSl76Er3/969i8eTPGx8exfv16PO95z8Pb3vY2qwpuaSVp/eMaxp2eRA1lTU1epVmrWWthJHPi7dQ+KpO6bRCFSNpftHu+0yKkNi6Gb9gAux5ATurtV0InpvGsa4mnhBkIaiqxXBYr7BTSTDtTcRRIxbocT+flpdulB5XHvUL3asvzQcv7OoVQVx7XA8pD0ErgCyrVWvefljtyuzw4s2M0Jo1FsVJp90upIZy8XOM7utAcBRaLZVp0dhGI0g4ECWru8IuqTaAqGUrmNFYlGWYUdorMoDiW43AsAmHa2ajrlDt1jkg6QrQZ/+QIML4X7Ij4GDku4JdAkyNqCyetB2onCqbnbjOrayFdz5canerC5/N8lr4yL/lkw8PDOOOMM7o2BBcKa9eu7clx4jjGG97wBlx66aWZx7dt24Zt27bh+9//Pl7/+tfjC1/4Apwp+p7t2rULZ555Jm6//fbM43/84x9xySWX4Mtf/jI++9nP4vWvf31Pxm1ZImiPcxioNDMX4DCd8HW0Ma8OzEjTcrndDK4M7ChU9aC5BYBfTNOQOZL0bNPA0cdwC5KK5nhijIQB4vFRTFz7CzgDAygfdzTgiOFJrlK2Vh2MKZn4XanR0i8ZkHFNjID9okQeAaC6XMbgeoDaPyPMwmZVMsyjydjyqXl5hwJoZu2I+oXrSTQ+ipR4zTQOBF23viAx6uW7idTrVlGIAbfYPvrcUpesvtt+UX4jNtXaYln4xJFkTjkq7TWKwNrojWJwqQwEDTE2EwEkyFzoGn3Q9byWZ6prenMyvYbUx2Su4diY8xScm2G0mnUc5jaLgbE9cicKkDi6a2PKEWtk/JiOZ0B1ezCyYjKO6YV6XbdYFj7219OBQw89FC94wQtmte/555+fGMjHH388vvnNb+K2227DN7/5TRx//PEAgC996Uv40Ic+1PEYURThrLPOSgzk//k//yeuvvpq3Hrrrfj0pz+NNWvWoNFo4I1vfCOuvvrqWY3TskSpDsti3yuk/Rzdghiw5QGpEy6UJZpmilM5TmpMOY4Yfo6n2j4VldHIqVKyOUk7nlKrVoqifql96q4WBfNLsm0cgWujIIox+LzTUH3mCaBKWVLMTFRrI44CcKMGzi0wEDMwOQqujQKP3g/se0z+djwgi4zxvaDJfdIfWUd/mVPxraRFVV7MzECrpiaobbVC6kLD9aW+fDFHRYmUGrcv38Fi55rzhNqo1OM3JoGJPSqqoxaNnv7+FeV/vXh2PKUCXpLv70L8PC0WSwrLNR9BHWhMShS2MQlEISiKpDxofB9QG8u5c/MRWMhvPon8qq3LQx0NTOZY5qL84/VJcH3C8KPqc5lbMlAfBzfrqp2TmlNbQssEFApIhLjy2V66LCiOkPUWG3NXsTq9g9RisXTEussN/s//+T848cQTceKJJ2Lt2rV46KGHcPjhh8/oGPfeey/+3//7fwCAE044Ab/61a9QLsvC7sQTT8SLX/xinHrqqbjjjjvwsY99DK973etw1FFHtRzny1/+Mm644QYAwN/93d/hc5/7XPLcM57xDJxxxhn4kz/5E4yOjuJtb3sb7rnnHnie/TgtEOOzuiz7WHmgdTu/KIuERk3+L+YU5lvUkHOCScWKePGJOhsvflG84nEoxypUMgYIT4zJdO44sliIWbW6AHjfCDC0HOz5IL8k59u3QxSq/VI6tjiSRRIzMDGS9dCr2+S4EllVkWDiUGp4deuQiX2yreMDrhLpcl0RYNGqoYWKLDjq46nR5fq2p+5843ozS7HOO1AAlcHgZn8XndS+LRbL4kCVAjEgWTNhE9BK0CpyzM0aUCiD/YLMI15RehKHgcqycZVT1xUHG1RdcQf1fGYGdm8TI7w0CHY9ULEqRvqOP8pGaw7LpmmHQdtWjARI1pWnotrlQaA2Lteq8oDUNTOnGiOJOJgS5gybapzK0ev6ci6CGMg2I2Z+MQML/Tqfpa/Yd9zgH//xH/GiF71oTmnXn/zkJxGqfnef+cxnEgNZU6lU8JnPfAaA1BtffPHFbY+jDe0VK1bgYx/7WMvzRx11FD74wQ8CAO6//35873vfm/WYLQcwXkEiz5WhmUccdaS6PNh5Xx0JrC5vm5LMW/8Ifnybvpc4wOPNdyH8f+eDJ2qgodVAeRDkuKAVB4PWPwG0egOoukwWMw2V3uZ4oKFV8phmYAWoUEodAnpMRaPNj+MCgyuB4bXA4ArZX0fgB1eKYVUZThdUlWF5PdVlqeFtWTgUjBYqpKLHOqpusViWBkSpcQlIRBksxqvuWBCFcg13HPC+xyXbyXHEQTq8Wq7hev7yS8DQGhHdmqq9HMdyrjgW4zVoyPwysS/dZty4Has2jHFkVB2lOh8c1FVVD4GGVoHWbgStUvObzswaWgUMLJe5p6icteUhEQ7T85V+rjqsjHxrIFssc8UayT2EmfGDH/wAAHD00UfjWc96VtvtnvWsZ+GJT3wiAOAHP/hBpi0WINHoe+65BwDwl3/5lx17SJ9zzjnJbWskWxYl6w9DfNdNiGtjEg1uTCKuTQDVEry3fwg02CYCnoMdN5uJVijLomdotahZA6lCNdBZUMWyNCiUgPKwqtMz2q3MRhnbYrEsXEpVMXTJFWNyYHlazjO8Jm1nCIAGlvfmnORkjWjdkq48BCxfB6w+RIzy0mBrhhVRmsbNMViJbZF1tC5Okjr3Pv5Z+oo1knvIgw8+iO3btwMATj311Cm31c9v27YNDz30UOY5nWY93XHWrVuHTZs2AQBuvPHG2QzZYtlvhPU6/vvr3wYd81QgChNnEcUhaHAZqFjqLh02qTlF2mpucBUoI3TC4vGPQpWitojrdC0ZOArBI4/LnzaEiYw6Q8Virs3uJVoxvD5uW13NlqAhIku10fZq/yZhU7bT/eItvUMr0fsFULEi2UaFEuAXVN9hF0mNbo9SVQlI+7UXKklPYyqUgGIZ5PrAwDKQQ9mUa1Jj0CU+OjOpULLfC4tlgWKN5B5y9913J7ePPvroKbc1n9dR47kcZ8uWLZiYmOh6rBbL/iSamMAdf3Metn77P+SBsT0iuhI05A+QtLd8bXUn4ihpN0WuC8obwWZbn8rQ1Ol0lkUF79kO/t0t8rdnW/qE2UdUC3ZZpB6zWVPG25jtrTpDuD4J3vsouDEuwlH18c4bB/U0Jbc+Lu+9pedQ7npOyrlK5BpBuN45yYgI5BVAuZRmyv0PclJxQCMCTbk/i8WyMLFFCz1k69atye1DDjlkym03bNiQ3N6yZcucj8PM2Lp1a5LG3Y7R0dHM/WKxiGKx2GHrRQJz2hcwn4qiU2yTnoGcqkLqlhEdj2vsu5hTXKZ8f1RfSMeTGqk4BlwPlLx2StLCTBVsifjGQBQDnqcEQXVPYTeN2hbLQKOulKgJGB8FyiIm8sdXvBrl392NQwBs/vJ3cPALT0XlsCPhVpTK59BK0ODK9q9HKV0nSp6Dq4Dx3TLW8qBEx7RBHIVSL6Z7HhcrSlU7bM24TnpKU+r1z5/XcQGtjm2+N/q9WszflUUKX/9j4N7fyp1HtwD/681iFEeB/GlFdfvZCHHcet+1/vJu4MYksCedn7lQBunrUZ6gAdQnsvNP/r239ATW/ew5EoPZK6Sq0UFD9BqLVXFquAWQp9oPcizZJ1EE+D7I8ZC5vre5ZjDHcn0JQ3HGlgbS8zuhzJ+emseI057s5KTZLW5Bvgscg70iMLpbWh4WS9CikRxF4swqlJUImQMmksccVxnoItjFcZzMT2T2YbZz0vzS0vGiD+ez9BVrJPeQsbGx5PbAwNS1lNVqKi4zPp71RPfqOHlMwxwA/uEf/gEf/vCHp9xnQcOxUhpWE1ppIL2IRGHq4dfGjEar33ZSsDT3XYg9cLuFGWhMpEZlaSA1HuNYDEuOZeJt1OVxryBq0npyJUr3ZwaHDdWDFrLY8ItKVXqv1FYVK3I/bIKKFcS1cfBDvwMRgffuBv/qZ+Dn/Bkmf3e3OiTj0X//NnYERZz2/148zeuJ056RxudBrg+s2jDFjgb1CelvqQ1h/T4l/ZcpPX6hrAzsGKgZ6qK6d3L+e+X61hjbH2x/2Lj9kPxPJN93Syu+UvdlpYZr09C7R1/7TIrV1scAURnWaOebv8id0gsQnhgBtt8HQMpssO5wmYP2bFXdCRzEKw4G/+F2EEeggzep67uqJVZOXQr9rNHjeC3Xcw6awCO/F6O3UAIfepx0ThjfI3MTAK4uA4VNydRoNADEaa/6+rjMHzpbqlAGhQ1wUBfjtzkJEInf+ZF75LHVG4BVh8gcWhsHxvaAVq5H0rpOG/VhIGMxnbuLef1isSwArJHcQ+r1enK7UJi6ltKM4NZq2ebzvTpOni1btmBoaKjtvouSKDTEmGLVxidK2w5pQ8g0ZHQ0FAAFjfZGctjMnSPO1hYtFnQPRU3YFMMPkElaRZjJ8cAlTxaAYVPquZJj5MKtgfHemPXCxQoyiWNqMUi6NmzFQXCe/FzgmS8EwgBP/u1tGPnZL/DI+8/HsR/6EA5643nTv54waDGQAchn3e7xPKyUT9v1P25HsyZ/2nDWx9CLqHyaahx2Nw5LT6HjngG+9T/lzrEn7t/BLAYcF6gsMzIg7Pe1a0pVMYgAMUCWr++sb+AX0vRqxxExOdvCpfeM7ESSFjS2C7zmUJnfHA8YVJlFIzuBfY+BjnhK20OQX+zuej6+J63jb9YlMj2wXG5XhswGDYLrSMZVBjVWx0luU3kArNspAtIe6uAnSDaWMYdTsQzwMuNQcbJPRoMjeQ3R4l2/LAb6ff201+q+Y43kHlIqpS1Gms3mFFsCjUZam5RvE5U/jnl/JsfJMzQ0lDGSFz35NJc4QkbF1jBuUlNPJj1qt78mE1npczpNL8mPOx8xIier/ltSfY/R5sKvjUTHVS11PKO3cSzGdxSAvUKabq3a79Chx4iyKDng8qDUcwFY/qIzUDr8MJRPPKm716PHb0Z95UZ3++vXPJ1IStvXnntPTMM5fdBOYr2CY9XaiwFftW1p1uS75vmpE6xQBv2Ps0CHHyOfx8Zj9vPAFwnWOJ4VVCiDV28U41cJRXXEK0i2UhzJbft+zw+FEqDlWFwfug6Y/BIQR2ByxLkxtEq29TwwCMScrAtYZRMRyJhO2vxGCmVpsVQoSVaSjkZrvQOSY5FZy+N62bmCWTmYzfIlBrkeOI4kS5oIXCipyLArgpSArG08aWfIzGoays2H+Xlpsa5fLJYFgDWSe8jg4GBye7rUZ1NkK59SnT/OVEbyVMdZ8rgqHSoK09Q2PSGp2mOOQtXHsKQENACAwESd2y7oCGkcSzRgsS5udMpp2JTXrqPmSS1yfoFHos5p3i9VVW2nquEtSmQ+ee8cF0wxyHHBrqcqpOTcHMeyzhhYLo9xDCBbI1V+ylO7fz06dSxsKu96ssSZ2fsR1FPDF8gdqwM6ApRpI4WsoW57JveORi2N2MTj8t3VGR5No5VTk0GlAWscW/oG+YVsts1UuJ7tVzvfVIfFaRGFQGVITfEkOhnQpbkueOOx2eu2ToE3I8aFcpp91saxQYUSMLhCDFStbzEVZuaAV5DzmYryjifZTQyw48hgYwa7XmIIy3DFKZs4oYF0/dOsyWvXY00cuHrs5m2LxTIT7NW7h5giW6b4VjtMsa58rXD+OKtWrZr2OEQ0rcjXksT15c8rAnVVy60nFC3SpdNmSwOSrqTpFFGkJVQ7RqrlBCnBqTAQY8OMtiOdhDPGokOG00EUOqkZA5ERVQZE7IRZvN9iDYsnXx2f1XMAwGFThE9cXwROnBlegjxf/hqT2cVGtylljpvWEDKr90G9L1q8LHkPKF1MdVIANlNWrce+d+RLJDr9Vm3rFIvlgIaYJdU582DOKHQIFLU+TqZhCci8P5WjU82byX5RKHNZFEra/VT2qFeU+aeWbbuW2ZwcwIWIckVBasib2yZjdoA4ltdvztv5FHHO54BbegZNIwA7H+ez9BX7jveQY489Nrm9efPmKbc1nz/mmGwUZDbH2bBhQ0bEa8kw1QLZxHHAxWom9YiIsjXHUagUJdVMtlQM4U4wi+OgPi7CU5MjYlzGkYh1JbYgJelcHEfyODMAR1LKxveIoEhzUp5TKWB6X/PzIZWtRqpWio0JmllSysgvAmET3KyJZ3w25D+7+vjMlGO1CFhzUqXxhmkaebqRvKBidXpxI45FJM0abb2hUEbyxSmUJa0xs/BTzgxb42mxHNiY4lpapMrxlMI0VC/iqmSTachR15Vyuq95uxMtpVjqfqmq1heRMTfmWtDpVlDJ3KWi2HlDyy+lGh9xlD6XX8/EAdCckIi5ub/5GnT6ucVimRU2ktxDDj/8cKxfvx7bt2/HddddN+W2v/rVrwAABx98MDZu3Jh57pRTTkluX3fddXjlK1/Z9hg7duzAvffeCwA4+eST5zDyBUqs1atV6tMUkxgHdWDXVoAj8MCKpE8iAiWCpuuSHFWTWhpc+qqueeEuIFdXCzDHSPpHNibURB4b+8e59ylnBGZUodXzSYumnBfcccCxpGZTqQoOm2KgthMcmQ7Hle9D0xCriwLA6dLxEbVJsQ6brRFjljYdKA2oyHNgqHsrNdQkLTiywl29wvXS6JB+P0uDQG00fb8dt/X7bbFYDixcDxhQ7QLNyHB5EOCB9H5pQIzovGBdZVl236nwfGnfxLGsJTy1hC6UUyO8nV6FeWy/JAZ82DTWJwT41Wx5V9UYlznP1sZy173cuEsDyGRBWeYPh+Svn+ez9JWujOTTTjttvscBIsLPf/7zeT/PfEJEeMlLXoJ//dd/xebNm3HLLbfgWc96Vst2t9xySxIBfslLXtJSG7tp0yYcc8wxuOeee3DFFVfg4x//OCqVSstxLr/88uT2WWed1dsXsxDQCsyAUl0udk6pHd+T1CBhfA94cFWqdO0VgGIVFKmaRkoqZ5c25ADIvVazxilW6cZOCGjbMI7BriPpXqQM5jAAvHYTvyMLBVUPro4M3vYA6OAjARAIKg1aC6NobzoA8gpSezVb8qnaM3F6tItAOp5836Kg/bZKiCzpBe24aS9e2cAuSnpJOxXzTIo9L31Hl8VimZ5O19384+0Mx5lcsx0XqAyqiLGXczp3uN1pXK4HmFON1+F4umQMEGPc9XNGstFxQZf92HnIYukJXa1Qf/nLX3YWOeoBZs3iYucd73gHLrnkEkRRhLe+9a341a9+lVGdrtVqeOtb3woA8DwP73jHO9oe5z3veQ/OPfdc7NmzB+973/vw2c9+NvP8Aw88gAsvvBAAcNRRRy1NI3kmk1k+DQppahJROwNvaXzfpkR707VwF5GqSVZ/HCl/AacpX44rNcbJpOuk+zmuHE8LXXkFoNkAHJmwOQzAD9wFch3wYw8BlWGw68IpDYgB7bhSG5YZ4hw+B0f1xo4CWbDMxOB23FTUjCH1ZDqVTQuDaaN4KpVw1wcKJA4adxGLvC1k4kg5zAyVcWZxgBVanYcWi8Uyb5AjrZ3mitbHiEOZvzo5/Jq11ChuTMq85TiprkgSGXesinq/sTXJS56uV5V8ANTa3XDDDbj//vuT+7t27Upu33///ZnILQCcc845LcfYtGkT3vve9+KjH/0o7rjjDpx88sl4//vfjyOPPBIPPPAALrroItx5550AgPe+9714whOe0HYsZ599Ni677DLceOON+NznPocdO3bgvPPOw/Lly3Hbbbfhn/7pnzA6OgrHcfDpT38anrcEM+f9IkRUKRQvq65v1WmvWpSKWYl3KeXHQiXJ/k2MsDiQbfTi+kCIQMVRqkqtJ1FHvWdxqGWoATDIcQBHtaUgSnYBALhuqk49sU8MykYD/Pg20BrpE0p+Edj6B4nWe2UADmhiHzC4XI7BnNRrJVeSOJQ2Hd2i+2LrNFtdlzqTCK753Un+1CJDp6drRdooTJW9HUci4loJFUjfV+3J7/fiRNfALeWFEbOkF+qMEsdYlMxU9M1isSxJOGjItbBQNuZ8lRXkuIDrSaeL0V2AXwQVynLdjCNwFKiyIidt6aXrf9tcVzkMxGlXKEnGFYBM20NTLyO5Rhu1wbn1CzsOMLZHzl8oy+MMMEfinKY0oZo5BmrjMue6vhLlJHHshjrTxktfv+st3bnBYukDxF1Yv9PV1/aKU089tS/n6cQ555yDL3/5y11v3+mti+MY5513Hi677LKO+5577rm45JJL4EwhPLNr1y6ceeaZuP3229s+XywW8dnPfhavf/3rpxzn6OgohoeHMTIysvj6JGvxKf1e6zpUvWguVOV+UEsNKN0LVy+oTeXKwtS9pJcMUSDGBaBSiKO0bQmzRICLut0F51SdWRmz0ksSHMsz43vTiJ7ng5sNkK6RqS4DP3KPKIgf8gRRuo4jkFLg1LDrq9rkCBjbDazZmNaPT0WzJucGspO+ajMFACgPTe38YFYCX7qFEGVrkPVxW9o7Id0+r3xtHpscibT3Y1ESNOS1ACoFcHhpLoaYgYm92cfyIjeFGThaLBbLkoLr42JkAjLHD6+RPsXje5PrO1eGwH+4AyiW4axcL9saYoAcBaLNkbT6i9X1fChzXeWwKdon2jG6aoMYymN70vriypDKUgqAyX3yGDnAwAq5XR9PI8PFCuJt9wJhAGfVwalRq+YgjgKQ44tjWo1L63oASNc743tUC0Ij7VqftzSwKOaGxbRO1WPdc/XlGKr2L5tpdGISK844Z1G8R0uFrlzx+9t4XWw4joNLL70UL3vZy3DJJZfg9ttvx65du7Bq1SqceOKJeOMb34gzzjhj2uOsWrUKN910E774xS/iG9/4Bu655x5MTExg/fr1OP300/H2t78dxx13XB9e0X5EiyFpGpPI1Ng2xpG0KGKtLqlUI2Mor65Kv56tkvJiRHvWdX9kXUero8lFQwQtaXNkvK9xpGxPSpwPNLRKosyxRFwdzwcHTWDkMaA+AVp5MMxcapnICWnBM5QQFsRQri6TfrjTfS71Cekl2dYwNcYeNqd3giQGMpAsdMwWGlpNXaenZ9prGO9Pu0WHFvnqphXVXAmNYjYdVe/HefsNkRK50Q6SnFMxagKwRrLFcsDSMMQbtRM3yTRS1CeB+jho5UHpY9pA1loZ2UKg9tdzc/3BsZzP9VIDGUjaTSbXLL1tHKZZUGpu4aAJOA6cdRvblh2S4+f0M0gcz5njRqmxnDxuZEct1blhIdBvcbRF4OxYanQVSbYsbhaThy4hbIrHVacJQ0XuG5OS5qQnuLE9QHlQJjlm8Q4Xq+lERE7qFT6QLjCTozJJawOU46zQUV6F2VR71oZhok5NYCJJyUarhgCHTcD1JM4aRWlAFpAJnZN7YJ3arff1iqDiFJ7YOJb2VXoBM9VnqBXQO8Esx0oueR0MXSD7fWnbI3kBRZLJESXUxfr9bk4CzbqkT5cHWg1hlc2QvOdmJOZAyg6xWCwtcG1MosaApDUvW6ciyXvSiGxpCPyHW0HVYdDytbKtGUlmln3MlGii1khy0AB2b5U7RMCqQyWSPLpL1iyAzAFaC2RyJN12YIVMg/Wx9ByOq6LYlJ2T2Zx/kXk8mX/N632jlu3xbD6Xew0LlcW0Tk0iyT/9cv8jyX929qJ4j5YKtqjLsvAIA2n1Asik4pJ4XLWgUj0ElwbFYK6Py+PL1kndckH1TDRFnA5EL6rrSZQtMfC0V9lMJXaQRHkJonatJ98kwqrSpj2/sy6B6yfmZqKK7fmgsIlEcTyZpJXRrf6dNtW6o2qpUtU2lae7Ee6KDFXQvONEvyeqx3OyUHF8qWnX6dbkynctaKQOAN13s1+LEb8o54yjbNuQxUYcpi21oqZkDWgBGt0ajCj7G9apjIRs31CLxXLgURpQ810EFCvKgUvSMk6JVpJfAI55FrD3MbBXkHnKL0rrSG1IgyTl2lVR5TbXVfKL4JWHSOS4UElrkgdXSgSZKG0F5RWkvVQcGgKQuWwk1wc4kuws/bB6TsaTM5pJHmO9naZYzmaCmdi2hPOHFe5a8lgj2bKwiCOJLOXRAhharAuQGpw4Eq+to4ybsGEjS4C8H/n6XCefjpWbsE2DM8kwVv0g4whJJE/Xe7dFiasVSrLIqE9kaoD1AiBJ5Z4OIqA4ID2czcdKA2kP7W5pWUS0LDVSQzmO04WFVwCCKHEaJFHN0qCMSy9wmpOA20evvecDWOxGYi7FMY4AREpwp0Mvc6K0vt5isRzQEFHrnB/HaQp0FAORJw7ZwRVybeEYaNZaZ6A4ECPZjMrmz+cXs+JcMgigXUZU/hqt566gIWsZvwjyfJkn2XDgOi4o7jy3keNlS4c4N5dbLJae0HMjefv27bjiiitwww03YMuWLdi7dy+iKMIDDzyQ2W5ychIPPfQQAGDFihVYt25dr4diWYwkKrZuahQXBwC/LOlTXkF5VyXtCOWBbB3PFBPLAYWZUpwRpDIMQ0cm6SSS125ypbRlE4HTml1ODebErx2FssBIotVKNKQ+oSKx7cY5zefFnPaINF9X2GzdN46BqZIGWmqvp1hMZFLRg+xjzBK10FHpFueDXaR0jeNK9kdQa8340LV2FovFMhNMgxOQuckrtJlvTEepmheCumyns9J6jeNmDWodvQ7NLCcPIGPObCsimX/IzIpSUWuvOIVD22KxTEfPjOR6vY73vOc9+NKXvoQgSH/cnXogMzNOOeUUjIyM4ElPehJ++9vf9moolsWM7hcYI+1FyHFa72Ogo5IZ8h7eAxWvkLakALKeZh0J9UsSlS8NKCVso7WR3q6TUJZR+8RhANI1oq6XpsnqY+jn8pFbcqZPl9XCIyZ+KSfApY7lTXMsnQoXNpCJik+5j9N6LgBA3LqvmxdZsUyLFlxjloWt40G+JyxRejNzxGKxWLqh5bKu5h2/lGYlaUd8y8bK4duYFGO2H05Pr6jEGFnmbr8E1EMkpTzmHOi4kqnViLIlVOb8bCpdW+YNImpr38zn+Sz9pSe/otHRUZx88sn413/9VzSbTbASF5hKE6xareK8884DM+N3v/sd7rrrrl4MxbLYIRLhLRPd/seMKjEDQTONjjJLxNkuqBXtjGNOhD94cgTcrImwSFCXSTpoqr+Ger+zlweeHEO851E5dlLHBenXSGmtMYdNadtTH1e15XE6ntbVyzQvwzHacnCaKeDmDHE2zzEFxQrgFjJGfiIMVSiraKb6TsVh+0h3O+NaC3dZZkYcGk4INv6ApEfy2G7VK9lqTFosli7IrwO0A9XzpV1eZUjKtCpDQHlYtQ/0kJlD4rB/1xzXU+MalvmpNipOw0Spm9N5CkjTtsuDrS3wmCR7a3JEBBEtFsus6cmq7jWveQ3uvPNOMDPWrl2LCy+8ELfeeite85rXTLnfq1/96uT21Vdf3YuhWJYCRFmDOI6VmqMDBAE4itLUzFhaCklU1KZmarhZA+s67jgGg8X0IEeUwIsVkJ5wo1D+IBkeIAJzLMa0Ol685X5wbRxUGgAr4RBRrxbla3gF+T9spAZr2FT2Tla0K/V8x8l5O6JbAOlotENifOtFUBSK4FgUdb8gyDtbwDLOxiTAYbogIeoQRc6hX09Qt+n+MyWTYp37ngDpojBspmnvFovFMhVEyoCspMJe5nNJP2En1e8oDSiBLeM61O/2PuQAzQkY6VytY4gCmZf09n4pF/E2sq/ChpHJZek5Wrirn3+WvjLnsNu1116Lq666CkSEY489Ftdccw3WrhWJ/eHh4Sn3fepTn4rVq1dj165duOmmm+Y6FMtiJo7ESCEHKFbAfslQuDYUmQsF8fwSAXVj8ghVv0IbSRYC6dHIqn5b5lpDzTmf5swh2PWAMADr9llhCHge4juvA9/7G2D1waBTXwrASPsxPe2OA3I8cNK2wskanO2YtiY5lu+B46XHcFz5riRGExsR4C7wi4lwS9JmI1lfmD2doZRJi1ljnkjG0y7SsFizocJAIhe6p3a/cBxRgE1EdqZySizWN9disfQdcgBvBkaFFgALXZkfvP3VNUDN2Ro2DObMNgZTXrPtddNimS1zdkt87WtfAyCL5q9//euJgdwtT33qU8HM2Lx581yHYlmsMAOjOyU9aGKvGEV7twO1cYkiQ9V+OI7835wUJeF2Ka8WgSMgbGZqZjhJ2Wp9nxiQ99fzlefZAXmuRIobNWDdoXCe+QIR6srvH0vdMTkOOHHCU/YPQLb3cPtxtA4sb4ROIb7VrZdVZyrEITLRAnLESNPp22GgohA+MmnA5KiWGzkDv1BenJ7eMJDfU9BQKqt9/h3puvhiVRamjoPkc9Wfd6FsHWAWi2V+IUp7r+8v0cBCRc0jZMyZBMBJI8f5a6Hjpq2nMscqW52M+WQRRJKDIMDPf/5zvPe978WJJ56IZcuWwfd9rFu3Di9+8Yvx4x//eMr9r7nmGpx55plYtWoVyuUyjj76aJx//vkYHx+f7bu2qJjzquP6668HEeGEE07AU57ylBnvf9BBBwEAHnvssbkOxbKYMSNIHIMqw9I7MAzAnpdTsI6yxpaO7NlFdEp5MFWthor8Mst7mlxolceaCFqtmuMAVEprwuPdj8J99plyJ4rkvY8CACJSxVGYGp2AGND5zybBMDI5ls9suj7JjisTfdBAkpqrBb/090AbdTPpmet6EiHO7O8B3pA4agCpX4tCUVY3RV5ilZ5eqKh2ZcqQi4L9GH2YA5mUclX3tj96i5ttVKJAnGRgeazdAtBisViWGmaGkleUmuM4kjIjQLWwKrXOM7ptle4GUazYPvIWXHfddXj+858PAFi3bh1OOeUUVKtV3H333fjRj36EH/3oR3jDG96Af/u3f2sRBrv44ovxrne9C0SE5zznOVi7di2uv/56XHDBBbjyyitxww03YNWqVfvjZfWNObuYduzYAQA47rjjZrV/pSKLosnJNr1xLQcGOlJkLpK1gRXWWxX98vcdT6k5LjLjZB6hfB2SitwSOWl0OTbUMdV7RzmFaBpamd5RHmkGAI7SmudZDdBIcZ52Wzf16mvxEl2TlRyLZlazajpa9P9hU15joSxGmaPS7lRkPfHug5QSKZB5EVpgZbFhLqQWSt2T6wPVZUB1uTWQLRbLgUNgdPLQXT0yQQTurJXhFUSErDxkDWQLAMBxHLzsZS/Dr371Kzz66KO46qqr8O1vfxv//d//jW9961twXReXXHIJvvrVr2b2u/POO/Hud78bruvixz/+Ma677jpcccUVeOCBB3D66afjD3/4A970pjftp1fVP+a8GopUr1B3lqJJo6NSdzo0NDTXoVgWMVwog72CIVeh0lszSteUjXDp1Ciz56BFJlSVag0zxZpIjMDEOFTbM6emHjkZwS4qlLJK9Y4LclypO44lspxxYnS6nUEdK46mF+6KY9WyycBxOxijMzBQtXMgn/atxb8yAfAISd9Nsy5ae+z3l9BLr3A9oDQov7XSwP5/DWFTSi50qxaLxWI5UDDX0to5nE/9nirThxbpPLQYIRIx0X79zeJzPe200/Cd73wHz3nOc1qee8UrXoFzzjkHAPCVr3wl89yFF14IZsbf/M3f4Iwzzkger1QquPTSS+E4Dq688solXyo7ZyN59erVAICtW7fOan/d+mndunVzHYplkcK6JllFJVkZbaR7bJMjk4IWoSJH1Q2pNEw7IWTRxkWmf2KujVLOQEzatsUxuD4BQFSrmRk8vldqkyFGNpsRU6+QGuAqCsn6eAA4jsBRBHZ1WvU0WQF52mURJK2akDVynRmk2+t+vKYDwfWyBi9IDMf8gsQvqdQ24z0lZ2EYmDNFC5iFTVmI7e/xcyzfX53WH4gjgpnBE/vAo7vBtTFgYkS1hZqhmrh+vc364oz6WyyW7tC9jsNGl/oX6tpQnwDqk3NXhWaWMqHGZNYZHEdpW8uwKbfNLKhiVea4QlkCADpy7PhKWNFLe8vHxphr43LtDJvpcad7Dcxq/3HJjmpMqi4P9tp4oHD88ccDALZs2ZI81mw2k1plswuR5rDDDsPJJ58MAPje977Xh1HuP+ZcxPmUpzwFjzzyCG6++WbU63WUSt2nxm3evBl33303iAjPetaz5joUy2KGOY3iJX13DTJiTWxTMKekjdhV0ntWjArOGNAsEeagifih34OGVoCqw2obBiZGgcGVkPg+Sa2uMhBJH8M0fvWEHwapwV400uHJEYM0CmQhMBVEkkpfn0Ri1NYnpogmd4nriZNFqYDDcUWMq56LXjZrYvz6pbTmWNdRl6oqmkyy70JIU54p9Yl0IRVH8poWEuJxUYJ+4/JZhE15rxsNWXwOLO/+eAv99VoslrkTx0B9TG6HAIo0vf5FfSLNMAKAWiB9lGfrOIyUICIghnplGACpOSY3d4VNmSO1o9Yvps+ZnRzMXslgQ2hSZ2dB5l2zhKg82HZuYmYRS9XHbki3h6QUqzw4u9d9INHv8qR5ONd9990HINWHAoB77703KYE94YQT2u53wgkn4Prrr8edd97Z8zEtJOb8jp95poj6jI6O4rOf/eyM9n33u9+dLNb//M//fK5DsSxSiEiMMK0uXFapn1rdUXtWdXRvOsPqQKdgLPxVSnR6P0wmWVLp11Kj7IAKJTgHHQH4xaQHMjkOMLA8vU8AgdK65vyfqntGHEt9c2UYqAxnU7KjUCbkMOiujtj1JQUtOUes9uP0MWDmUUW/KIugypCqaXeyxzAFyPxi2kdTo/pNS4/KRWggA9mWVwuhnyY56e9bK7bWx+SXX6qCOAbptHa/MPMxm9vP9PtisVgWB/nfdjfXiZbrAc/tGpE/Zxwj0yVh2vN3OI4mCrsbXydn8uhOcfLqzD0lE8JTndOyIBgdHc38NRqN6Xdqw44dO3D55ZcDAF72spcljz/44IMAgGXLlmFwsL2zZMOGDZltlypzXtm99rWvTVKl//f//t/44Q9/OO0+zWYTr3/963H11VeDiPCEJzwBL33pS+c6FMsihsqDoNWHglYeAtLRuoHlIt6j+x9rg8YKUkyN50uUVCtZm/2KdbqW6WlWdcgAQIPLQQcdmcaFHRfOqg1imMgDSCK6skfr+TkW49pxswa0xvUl8uqXAK/LjACv2P5xcxy9+F60y1DoZAAHDUn5rY3OTDRsIWFGLPwO73G/8Usi2FVWUZxI1F4ZaPNdmmEy1EJ8vRaLpbc4htgjuogiA61zjOvNzfmZcah6aj50cvOUIR7Z6VpmXqfM8RRKgNvudRnXR33OHKwz92JO1lcMpJolnl1jLWQ2bNiA4eHh5O/CCy+c8THCMMRrXvMajIyM4MlPfjLe+MY3Js+NjUkWRrXaOdNqYGAAQKortVSZc7p1pVLBpz71Kbzyla9Es9nEWWedhbPOOguvetWrsGvXrmS7u+66C48++ihuvPFGXHbZZXj00UcBiODXF77whVYFY4vFMntM9WeNrhsGACZwsw6M7QKXB0EqZZXDQAzs8pBqBaQmzYq6X1NtKDgWl7NXlIlWExm9h1XbJAJUejSyRo7jyDi7Ef3zfIAqrenQ2mAvD/emH6RflNcWGgqjHKOtPzGoG7cbi9N5UyipBREt3H6ajgtEYddi6FNSKKvF6wJ+vRaLZW4QiRihOYdNR3ItVOVD3e7XCccFKstax1AoA6zaNYGmH6NXSPU2dCaVnssdD+CCjFlPw0kniM7HJSKwDj7ox/RtxvSCmhah3yJp6lxbtmzJiB0XizN3+L7pTW/Cz3/+c6xcuRLf+c53UCh04Ug6AOlJY9mXv/zl2Lp1K9773vcijmN873vfS4q5tfGri8M1zAzXdfH5z38ep556ai+GYbFYNGS0a1L3JRta9yomALGktodNxPseAxUq0n7CL4BrQZr6DiVAUp8EStU0qpy0R0Ia0TUnDGaQntw5FnVGMwDN6M5QiUI5v27b5BWMfs2KoD47lfM4kpovs/1YPtVMOxbiSPVrplQwLqkFi2Wci7FXNzniFAgieW/312vQQjdxmPbB1k6XpE81UgcNMHPHhP4MQUChmGZZhM20rZdn9NC2WCyLE6JW0UWT5Hqusq3iUBmYShCrF9fCdmPIP9ZNT3pznsx3+IAjNc9RqK6LLK+jYMxRQUPmS78IeAVw0JBjGsKdmWPm1bQtC4qhoaE5dQR6+9vfjksvvRTLly/Hz372M2zatCnzvE6xnpjo3GFifHw8GctSpmeroXe+8514ylOegr/927/F/fffnzyujWTO1UUcddRR+Nd//VecfvrpvRqCxWLRqDZQIEdFYUnNnwxEAXh8L5yBZWDHBRXLQKRqm6qDqZgVQdKJdepVoQg0a+BCWcxkIhFH8QqpsaEf1554qOPqiV3bHcxiNE8XH2QW5U1TaCyupWnR+vH6mKphnYFHNQpFEEqrW8exim5HWeMXnBUhM8dmisnVx6UGe7EZV2EzjYpHQUehl76MIxG6aWafY07TIf2Cel4Jps0ELc4DyP+lqny2up0XADSVYW7TsS2WpYkWgMzXB5uO1yhISz4WMmEjnbOTeStS1zcldKjnrrAJlAZAzRrYK4oN3aynjlLPl7nVttXsDjM7r1/nmyPvfve78elPfxrLli3Df/7nf7YEMAFg48aNAIB9+/ZhbGysbV2yVsPW2y5Vehoy0A2mf/SjH+EnP/kJbr75Zmzfvh0jIyOoVqtYu3YtnvnMZ+JFL3oRXvayl8Gx6W4Wy7zAux4FikVQwWiRpVo6oT4OKg2oh/RzSCPEyWMu2PGkxljVTLHjpQayTrfmKDU8tDHsaBGsnPK1Rp9juuirFpbKqJtDFjDauE22nYHYSBwBY3uQWSRxJKnjprGvVL8R1LKToW6jZb4W3ZJjlj3j9xt58RfmaX0XfRmHiRbq0nV+3dQYmmhnh85GMM/X7rxW1MtiWeJ00xaKF76RbIovGvMkx3GSmWO+Ag6bSqjLmLdcD0BT5u/F6Oi1dMX73vc+fOITn8Dw8DD+//bOO16L4t7/n9l9+un0jgiCiopGsRsFVKJiAa8aohFbNIl6TeJN1HtzseRnTEw0zShWbFHUaPSKYgdFVMBE0CBFBCw04RzglOc8bXd+f8zM7uw+5Tynt+/79TrwlN3Z2X12Z+bbX3vttbyZq8eNG4dYLIZ4PI4PP/wQkyZNytrmww8/BAB861vfatc+dzZt7lfHGMMZZ5yBM844o62bJgiiCOyv1oBvXgdjrwNl4iNLxB9xISSwsr5CWLZSzoTKpCWYZ9KO5ZkZpvSKFsK1ym7trC04F9tyWc7JiVMOuN+LF8grdTU5GXOvIG1b8pja4sW2hADbnLJgmTSyFkmhmOt6rffPEaa0YwbD3rIcuRKUdRcCIfd6tDZZTav6ERYWbXU9daWIYbY85juTdsvB6KXD1P0SCPlqqRaZ6IcgiO6JCttRHiv+ygaATHLZDQw5/rkIcmZL1AO7tgKGCV7aBwwcPNEAvmMtMHJ/mSvEAKsaLOetvp3T/+5MJ8Ukt4Trr78ev/vd71BRUYHXX38dEydOzLttKBTCaaedhmeeeQZPPPFElpD8xRdf4L333gMATJ8+vcV96g50gxGAIIjmwDevdWJ2uWW5Mb07N4uyTJAhwYbKdG0KK10oIoSIRIO0COsDsqZ1TidFZkx9AWEGxV8wmDs+Od/gbjShp1OxUamkjGuW2bWVSzdkfFcw0rwJRCoChDt6SAhohunGO3u29QlMKgOoX2hrbaKXzsIwhYt1tEwoCjrrHAwDiGjujY7iwWj6PimEnmBN1f+MVbhhBIYh3Cqj5fK7corJI4ieTigqx71y4ZbsH2O6yxhgmO45xOQ4FgyLUClAul5bQjGucnukEk3HbBM9hl/+8pf47W9/i8rKyiYFZMX1118Pxhjmzp2LV155xfk8Ho/j0ksvhWVZOPvss7Hvvvu2Z9c7nW6YZYYgiIKUVAo362gZmGECpiFiLiv6ejTNjtu0YbjWw1BECNV2BjACTn1kLkvwMKWB162rqkVHG68JWeEYAMNNvMQtN34KkNsXmKgZA6KlIqu2x8LoE8RVEpZQkdZkwxTZT9NysQAurlEoJoVkXyKTYMSrrU8l3EzY6pybK6h3JRylQydjGIBbrdPFzoj7siWlSQzT/e2U0J0z83sXOH+CIDoO3WsmHBM5EVRm53RCKlO7gS1JF3gZZJ4QOVdK5TcDgPK+4LYl8pAArRtXiW7B//3f/+HWW28FIHJB/fWvf825Xb9+/fD73//eef+tb30Ld9xxB372s5/h1FNPxfHHH48BAwZg8eLF2Lp1K8aNG4c5c+Z0yDl0Ju0qJG/fvh07duxwAr/79evn1FQmCKJ9YKMPBb5aDQDgyvU5XAJmW8K9TJbUcSdGb0ZqmKLGJAcXccrS1doVrW035ljFJqtMmXoCL0BM3MpV2jAAf2WJpuSSTMqbYIXDFXL8GTmLiTFTWBkgk5DHVyU/mIg9zhLQfJm0AXHuqbhXSZCKA2Y3SPTSlWFMWHcS9dnujy29rqEonOy1TCZmMwOAZQk3azNA7tUE0dtxBM3uX/6IGSbs8gFAzWbACHpcRlW5R/eDDu1az4IZHatEacGxampqnNcffvihE0vsZ+TIkR4hGRAJmQ888EDccccdWLZsGRoaGjBixAjccMMNuOGGG3Im9OpptLmQvGjRItx///1YuHAhtm/fnvX9wIEDMWnSJFx22WU5g8EJgigSK6NlYubgqSQyu6pRO28uysaOQKCsEkZYWFY55+CpRhF3bGXAAiHhYu3LOq8sxY7V2EETBlUGaEO6ZBv+hF8F3KxNWV7HkrHPhdxoM2kZI6yV6FCW5VQS4BnfcZsxgfjOG8yQMcm+DNahPNmTDSN3LUnOhXBnWWLxYZjdx22vq6Cs/Mm4m2grGMp5HbltCS+DUBTMlN4KZjD7HgwEgUbpdp1JCauRckcEhGDeRQRlzm2gfjcQCAqPECNAipe2xMq4Cr5iry3ncvwxu0ecKtEylHeQbUnPoO75W3MrA3z5b3Gv79kBPnK8UHjbGTArAyDqjoutCWUhujwXXXQRLrroohbvf+KJJ+LEE09suw51M9rs6di8eTN+8IMf4NVXXwWArJJPim3btmHevHmYN28eTj75ZNx3330YPnx4W3WDIHoH6QQQr3Xfcw6rdg/+eeZ30bh5O6KD++OwOUPA9mJg5X2BdBJ8+yYheJb3A/oPF5Mjt103aVnSiNu21/3Un+HTiTk2fO+ZZknmUljxLTIYky7RRbhFq9hgXQiOVQhBO2y4paFUXcfmCDlmQFoTM65QptyqVRmiUMxNEmaktFhluZ1peUsHKTf0+B5vv6NlLU861VsxDKEMKQC3bfBNn4jfIBgG+g2TyWhMoLSP9571x5n7FRxWpksIyZxz8M/+KTLKDxkjlElmqMlrQRRJqlE+s1q8e7SssKDMOdBY63o2RDs+Zl2MyRAJFon2g7GeUf4o1egZ43jDHqFwU1gZoKxPJ3Ssh2FoRoKOOh7RobSJkPzZZ59hypQp2Lx5c5ZwHIlEUFJSgoaGBiQSQpOvtnn11Vdx9NFH46233sI+++zTFl0hiN5BMu59zxiwZhkOu3Qyvnp3FTa99TG++tPdGHndL4RleMtnYrt+w2EM2cfdR68RHN/jWlgiZcL1WL0PlQjXVGXNjZSLidZKS+Ey6ibDypW4qyUEgm7SJWYAJeWuFUclK9HPvzmoc/QrAAJB4TKtt8mYSIrid+9WGZf17Nbqd9HbzKRISG4Pkg2ukiIc0+5l6R6vC71mQLs3mcyknRIx8kCXEJABCCXWnh1gw7VkKCoLPVmTW086AW94ie263+dDWZ6dNpIdKkjZm9eDr1wEMAbjWyeBDRzRYccmuinhEjFnJeoBxsDK+4txROUD6QmKAILoAFotJKdSKZx11ln4+uuvnc9mzJiBWbNm4aijjkK/fv2cz6urq/H+++/jkUcewXPPPQdAWKDPOussrFixAsEgLSQJohi4bTtLPQ6IRfSOrTD2Owx7nX4JwgsWITJ8GIxDThAblfYRCz2/1US9NgygpBKwMiLZl2EIgVF3MXTeG66AaIdcgdHfZmtRgjC3crtFtsVxcrWRr9182+qfq+vgsa6Tu3W7EIqIJHWxMnHfOr8Dy3YhZDKDtS09B5gBlFS4ngRdxYU2EATK+oCbJgyZMI+Ru37bkEqIOujg2eNVskGWmgvKEBapHDS1BEhqW9vO2Xx7wT993/GEsFd/AJOEZKIJmGEAow8B4nVAOAIWjIh7WOUkKaQUIgjCodVPyv3334/Vq1eDMYby8nL8/e9/x5QpU3Ju27dvX0ybNg3Tpk3DwoULMWPGDOzZswdr1qzB/fffjx//+Met7Q5B9A4S9eDhmCiCxDlgWTAOPBLGXvsBjGHI+ecJbbKEFeOuqWKU9PdmU+/bebI1DHSnSnXcFm7rTMVsNzfrNlE0jBlglf0BSEVRICyE32A4t9Cb6/7tcllduTinQMj1yvInMCOaB+eam7WESaVfIOSWyANct3xVTzuTFKEVzm/BgVAHCxjhKJDUPCYIogiYYQKlldoHTIyNRNvRDRJ3Ea2j1Vf8qaeecl4//vjjeQVkP5MmTcLjjz/uvJ83b15ru0IQvQZeWwNeL92jbVuUeBi+jzf20u7+WTq7FVxmuLZtsfh24qSbkXWbKB7N/d2JRY6UdG8riTolaRVn5GLdetJJr4AMCGVJKFpcbLEKt1D7BNpX0OC2Db5rG3idyEprHHoSMHgUMGQ0jINPaNdjEwRBEC6tXk2sWbMGjDHsv//+OO2005q172mnnYbx48dj1apVWL16dWu7QvRUZEIpbzIpWZs23/tWHc8Wi1XOxepbT1Cl3PDAinfR5Fy66ImiSqJ8UAKIxLznl0qAy5rFLJ0CZwzcsmHHEzCrykUbtg2WycDeVQO+aS34yH1hDBwGpuogWnqConbQOup1kP2/SU8hV0x1rnvQT6JeWJCZuy/nXGQSJ3I/o07N6xbcR4YprHzphBCMe4C1nhkGeFk/oGE3EAmAMwYWLul5z1hH4lcWGiYQ0gTdQEi4oVppzd1aJQQMuNnSO8gKxz//J7BLVgYZMR5s0CiYh57UzEbkc6VbnvKNYfq2+Z7RXPupBI9qG+c7X1vOMeR3Th30Iv532nW0RzKRYo5a50Tract1VG+go9c/9Lt0OK0Wkuvr6wEAhx56aIv2P/TQQ7Fq1So0NDQ0vTHR++BcloLJAJAa/XTCrVsbKRULnExKvA+30pKUTgirg1oYSPdZsQDn0n02JeN7y5vOOqtK1ICDZ9JAfLdwo0wmYQwcIV7Ha4F0Anz3TvBd34CZJuxvtoCvXwXU1iIxYDRiB+0HnkoBS98B6utgV/UDD4ex+pr/hwM/eMc9niO4M+9CsC1IxrV6wXLhwgzxG/SUwTuTcq1OoaibsTrRIP43THGP5Tpf5bIJUZqIGSa5uClSjW4SNmWVsy23BrYZENnEm3MfKQtfpGdlfmYlFSJemmgbghH32TSDufMyRLtGvU/OuSsgA+A1W8AGjWpeI1ZGJGwCxPilktol6t1EiypDt5Vxy96pMnuA9xmV8xfMoGiLMfFZJgmRBC8ghVnD666e9TqXRw1zY749Ahr3elUoQV5XqqlKB0TbkE66Y7Sq7EAQvZxWjzBDhgzBxo0bW+wWpvYbPHhwa7tCdHdsW0zY3BZCqRkEGhsAqJg8LgZyJahxLmqdqpg9ZggLbbRUfJZslEmfgu7kbwRElkdmCkuubiVONcqJX8KktdiZmCGT/DDAZqIsSFNCcibl7MsCQXAjABaKglVocW6hiFgbDB8HNmgv8LpdMPadCH7oJDAApaWVrkZ9wvGwv/kS5tYvgA2rsN9VZ8La9jUCg2UZNSXYKQGtreC2JiDDvR7q855iLVWLBPU6EBIZh9U9ZluyXFCOBUQg5MnGzSOlIoFKb48j4tx7XVMJIAQgEYdzHzm1aylJFdHGmAGgtMoJS+nKCj3GGHh5X6C2WryvHJC9kWUBqQbx6ISj2Znz9TkskxLzi2175810UlwL9VxyDqfuPCDn0aBQCDvPaBpIyNriGdkWgxavrcXOe8J+8gjIHG5JG8bkJtxtzzkAXO8taJblVGOXUW60Gm4LJTS3hVKnM+ZTTckr5j4SkpuEYpJ7PK0Wkg8//HBs2LABK1asaNH+K1asAGMMEydObG1XiO5OutEVZpNxIBCBU6IFEJOjX1Dj8GYfVRN1OuW2ZaXcXdQCgstkLaGoeG9nvIsL2XzO+sBGQHgyZ9JoEjmoOUl4wjGwXJZFNdmHozDCsk8VIjO8UiRxLixuxpDR4JUDgH0Ogb36Y4RGjnPdfpRrYJtToM2eNHAzw73n1Hn53erzudlHy5zFDQuGu/RivMPRn1FAWpBzbEMQ7QEzALN7jFNsn8OBmi1CSKkcmL2BynsAiHky5vM68I/HqQSyYb447RxCbGMdssZ9rimLbSu3cdi/LbKb8XymFMB5XcF9Vn9HoO5BpJOuYiHVKEvWtcH9qkK7wEWZxkIhYp4s7t3jWSGI9qbVT8Jll10GAPj444/x5ptvNmvfN998EytXrgQAXHrppa3tCtHTUfVPFSo2CUxLwFJg9uQFvtM133rbzBATlmW5LmGGKePaioiBVIm11L5+7ayVAZOfOUsB7mrTc4oNZhCsrA9Y1QCEjjkJLBQRLr2BUPsJGnrymmBYaLtVIpue5PIWjonrqNwUAe08g8INLV+yH8bEPRGKkMCno2pSB0KipE4m5bnHASZd2GlhRhDMNMH6DwerGtS0h57MZeGEGwFiCuS2+3xZaTlvMpnXwnLjrvWs3bol2Ep7w5iyOslEAjP/fJYrRpMx4c2ljiM+hBNfDLjzL5PzufO/bknOce49ljaaP5JxofzPpIBkfeFtwyXa3Bdtm+MTRDen1auSyZMn4wc/+AE455g5cyaWLl1a1H7Lly/HzJkzAQCXXHIJTjqpmYkpiJ5HMCrr4RpiwA6FpUaVeSd0x33Ll2DJccmCLANToLauKhWjsH0Trq5x1TXcaiFfZMIGnkqI+FQ7I3YRn4o2LQvMDABgYGpRwLljdWbqX5VETFoPGGPO4ol1pIBqmEJwDEqhXAmUUP2zuv/ChRlCIA5FvUKbEppzuaBxu2ece3uiYrnV9XMW5EyELPQkRQtBtCcqLljNC8kGYfVVrtNWSgrCmvu0nliLcwC2TPIo55VUI3IKZgzeuU4f48IFsoP7hVs7I47nSe6ltatq92bNqznmWU8SsB5CMOyGAoSibadk9bi9N1FKzsgz9xH5cdaFHfhHdCht8iT89a9/xU9/+lPs3LkTxx57LC6++GK89dZbWcm44vE4Fi5ciEsuuQTHHHMMqqur8dOf/hRz5sxpi24Q3R3DEC6rsQppFZVJoWIV3vqQhhAswcxswUSfnCMlIkGJitnSv/NPBErQAaRAXkx/i4ifTCe0eFZbhl1xsWhgAAf39guuAJx9PAPMyjj9ZAC6RAynSgqTqBeLtaYm456ElRHnnKiXMWU9aOHWHphB+fxKomXFPUcEQQisjJbIShtrVfiPPgT5hSS10A7KUKaMtBjDF1vpKIMNbwItwFVYq0RguVCKbF2gNX3Pub+KgD52+sdRfx/0PvYE1FonWt628cihaO7XBEEURVHq+7333ruoxoLBINLpNB599FE8+uijYIyhqqoKsVgM8Xgcu3btcqxknHOEQiH84x//wD/+8Q8wxvD555+3/EyInk1Qunap5F6AdOMKACrrtNrOj+WvF8yyLVehCBDfI92sdVdqNdHnEFz9Ltq5iJYDOzYB5f2l9Vhm6bYzQCAMppe5cLT9zF0EOYsCpXE34K4YZPKSznaNsjPaYo2LhZPRSzI66+6IdsZrsSGyYQyIleeOPyQIomnSydyfK+EqFAEa5bjEDJHbQ7naMgMoqRLPnRkQ85F6JsMxZJVtsi0RA61Qc14+z49wiZijGuu9AnwumAk3KSc0Ydm3n5N3RNuOlJHFEQhJIwGHk3080SCuXzjacxJudhaUuKvHU5SQvGnTpqKzV+tJhjjnqK6uRk1NjZu4CK67aDqdxhdffCFqidJiqXdjZUTiLuXu6h8MbFt8758bzYAY6NVk75+89czEgNgmmqNkkWGKJCnJRp923PGRzrFP0wMWi5SAD9gbPNUIgItsx8qlWrlMa3UomToOY65FVt9ODcqMATDc7KCdif+3KrZ+dE/AML1lsWgcaxoSjgmi5RgmYEmrsBEUVmHDcOe+YBgwKsX8oYQgO+KOU+mEmGOj5ULJquZcwB3L0wmR/NIMCMFXKY9tS8yn6SRyulupOQqa4hcGgBwKZVOeR058btkwAObflsaQotDD0JKN7nooGW/HRJ8E0TMoOhCMt0Jz59+3NW0RPRDluqVPuuES7zapuDe5h8r+qJJV5YoVtW2vFlyRTxvHuSuow1D+zN7v9Zhmfx/zYRgiTiwQBpeCFAtqGtxcx/H3y3ktN1XWbX/5j85AxZtaaWHZ7wp96iicmGy7fROnEQRBAMLiq4RUMyjGXRWfrOfvUMm7rJT4U/NIOgGAaZUeZAZk5ZFkZdzs81ZajO/BsDe5Vzgmj8u9SmX/8MeY6CcMbzIx5/Nc+GKSORfKYHWODrSObDb+eG+CIApSlJC8cePG9u4H0evRJjzbFhZgw3TrFOtxrpx73ayVG5FheOMbuU97zZjITmxlxD66xdO2XHdrbslofQNg3Dexa20VM8lk0mCpRvBA2GMpdrwn5IKGq2RcTnIyGzAMRyZ2rhEDuPwOhiEyW3cFzEDvTL7EWG4Xf4IgiPaAMeFSzTnQWOsKrqGosCpnkm55p0zSzVuhx/76Q4VUqIidATK+8CQrI8a4ZNwViM2AiKG1MtmeVzoeBa/MyWFZgBkAs23NvZuD6/k4suKUXW8r53NyPS0eVWUjGIFzLZUbNgnLLccwOtZzrjd56XURilrVjhw5sr37QfRmVCItlWHTzgAJWaMxUgo07AbAhbVY1bFN1HoTAKm443CJmNCtjGtF1oVb6fbsbKsEO7W/in1Sqaj1uoFaKAFLSXe0sj6Fz01mGHXCEAA3NtkMiENZFsAscFMrBWWKpGRMbc+51+17zzeivT5DwCKlhftAEARB9DB8ibusDBCEzFqtNuHZITkqLMmf/TpRn7vkk1Le6oK1OkZW/LBPma2/tm1HHEZjPXhZX2izohCimekNvXOEYnjb4jYJDMViW9JTD1KpGxPrp0xKrJFila4xgnJFEISHXmj6IbokwYiou5hJuYm5wIV115AJumxbWHYZg5PZ2p/wKp1w3chyos22maQrJCsBHPB5e+WeLLhhguVLoKJjBrVEK3ISSjaKiSta6nzOwMBVLUu/67haDDgZTblMBGaLzMokJBMEQfQy1Lwohd2A9GgJhIR7NSC+D4Szrb25hFtduNZLHVoZYbHWJ0aVqdpfXaGpklCAsFTblpulW0tOmXO2VQKxJ8M1E/MqZWxuGsunDEnWw/ktORdrLlv3CGCiMkhvCptqIXo5zo46HtGxkCqO6Do4ibeYnKTlZG743MU0q66rrdYSfeglnNT3TpyW5j7tKVUk3GY5mCepltOudJHmnIOBi6+LcbM1TMDm4JYFnkyIWC8r49ZEBMCNALgZkDWPuXMcMOZVoEOK08wQgyUzpPsUQRAE0atgMieFZcmwXzlPKgFWheU4LteaK7MUiLjWlifCl9taDLHm8qxQVmXb55qtW5u1BT2XrtZc7aMJ4aJV5syxnnlaCdOqfJX63LLI3bpY1H2hr310a3EmmV3KK5XouP4RRBeGLMlE14Jz1/WHwauZVq7QuhsX04RjtxHvR7kSxenuywBgZcDj0gXJDAo3aGXR5QC31ULEndSLUerxRD34ji8Am4OVVYBbNlisTAjYjAHcAvPUj2QyHtsQbmmG9xH1aBI5p3jY7gTnsm62/N2oPjBBEC2FS08rwHWpjZWL8kvKbTqTEski00khnIaiYNxyBVIwcCcXBgMPRcBsS+zvnyNz1THOSjKplQJ0JXA42bEBkfTLtqHikOXW8jwy3jmPGYBpeEOfuCYwE/mxMuJ3NEwgGM1OYsoYYIQA7i+RCVJAFIsyVnTk8YgOhYRkoouSI1mW+pzp7sdF4En2keM1AJ5JSpcyCEEmVg4R4KVN4NLtmTkLjCLOomYr+NYNMMYfDZgB4brhsYTn6S63wa0MEMr/iDLDbL67mYqH9ic5641wW16LQMfEt6UaZWZZiMVLrIImPYIgWobfZVq51do+1+p0Uub4ABAIgWvjvl5+GAxiPskkxTilSjnpGytLsXLN9o9fTLNaOhK2nR3ClG/cy6kA98FU6UMaO/PCbfc3B7JdpxkTOVkyKSBjwV1vmWJdQG7sBAGgHYTkV199FW+88QZWrFiBnTt3oq6uDrZtN7kfYwyff/55W3eH6G7kFRpEyg8n07Xfopq1ra+9XJpvfzIQHVu4mjmJtmwbLOAuAIqODUklgWAYLBhxtPfMtoWQrRY1XBPQuCW061ZaLESCYQDSquyJyYKIO8ski3e55lzEe6uFTijqljDqbXBbWFzURY2Utr/SQHdF9C9wCYIgmgXzWlSVoo+ZAOTcYqW9LtHJBlEfGRBznBlwKy4wUyaKhFAc+q21yvVZHRsQY6ZHsNWVjVrSy2QSCAbFbqmEp4azk9SScxlPbWneZMzXnjafU+Ku/PjX3Hq8MTPkfGe4MedcZr5WHm4EQQBoQyH5gw8+wMUXX4x169Y5nylrmy5Q+C1wyipHAekEAG1wl0KxpyQEE65XtiUFZW2SZNqk2lwLMyC0qoGgcHVW9ZdVLxprgWhZ9v5FHIcN3At811bvsRgDAwdXgpK+GDBDotQGOBCMgEmrM7O5L2ZMCs3pVDPikrkvQ2mm9wrJlqY9B9w48fYkqCXQCdBihCCIVmAYbsknQLgxq//VZ2YQiJQBuza7cwbnQKQULCncb5XoqeaaLEuvGRRtphpFokgAiMhj2b5xlGcAmDJWOuPuH9QyLJsBsHAEnBnwRUKDOcKvL2QqGBbHpyGzOAzTm9RNwZioh60UDIYp1jaJepn0NCUSipK7dXF0dCZwWjN0OG0iJL/xxhs47bTTkMlk8grB/s8AuAmKCCKdFMKhLhRziMlRxVf5E2+5ptWm2/cLz8ybRZoZBnj/vYSmPZmQSbTgLChyThrFDFiREhijDgK3LTDDlM+DDWYLxRC3bTH5h6LugBuOApYFpme1VjFZ+uNimM2sTSy1yEo47411jRV+gbgjrkUgJMpt6CEDBEEQLSUQ0sJnAu5nZbL6gZpz+u8lBNx0SgjDhqlqKrhlBmG7c2SWFRdezyM1fvrHUeVubVvu/rYFZmfAzQDADDG3mgEhEGuKcGXRzi4jxbP7w0HZlwvBmPht1NpJrS24XG+ETVdQtjKu8pzbYh9KCEoQANogu3VDQwNmzpyJdDoNzjl++MMfYunSpbjwwgudbTZu3IiPP/4YL774In7+859jwIAB4JyjtLQUjz76KDZu3IgNGza0titEd0XVaOS2nFwNYWkLBL0CMZPWZUBLmFDAndr/XteS53A1ZukkWDIOBhmraos/BohkJrbta6+Ix6exVmhoE3HwTAo8k3EEZa76FAiJ5ClONw2wQMA9N5UqTNVuVjTX3YwxIfCHotJy3kutyIC4dpFSsRjoCFdr/bhaZnOCIIgWwW1ZLlF6CKnSiSpBYEqWGuQcLNEg5jdwIQQl4uJ1rmRcZsCrrFbCKLeFMjuddD2+skpJafOwnqHaMN2qDKp9htzjIJPlnvTEm373YZbj2ISLbYnfP993aS17tX8d4S/rRRTAkDlmOuiPChJ1OK02nzzwwAOorq4GYwzXXnstbr/9dgBAWZnrnjpy5EgAwAEHHIDTTjsNN910E372s5/h3nvvxaWXXoq///3vOP3001vbFaKnoEpUqEnQMMQkqSZKpSXN64WgJw3Jg5WWWlbp8GVlnIlD7CnduXWXJb0UFTPAg2Fg1zYp5BrAzq8Bw0Rqy3akNn6F2JihsMMxBAcOFNZh6c7GAbFQcFy7g46mnOklrUx53sztleca6f8XixTKexVO/DcXCz51zQyTkpe1B1ZGuvIH6foSRHvhn/6UNTDV6GYyzqSEwjlR730WuaGOSCQAAGu4SURBVJV7/lRCrSnnJOVtlUoIF26nfKIllIvppHf/TDL3nCSzWRfl9ZXJiMRczhxn5hCIGQnJhch1vfJhmG4SL2nlJwhC0Oqn4dVXXwUARCIRzJ49u6h9otEo7rnnHliWhQceeAAXXXQRVq1ahUGDBrW2O0R3hDERN5VsEEJjOObGLwFibg2XuJrycInUkubRYuvtqthmP6p9JtzRxKSiW6oN4RqrSmw47clDGSb4V586WnVeWyP2ScQRtC2YDevB/r0BRkkleN9TYUgLNocKQVBtqqY1q7GevVvFYmXVipaWdCoB1TTppBujZ6SBSEnn9qcnk066z1aSASWVlGCHINoF37ymFMdWxruNUvKqhJfMEIJQstErEDEmnl8lYBsmwANAWr5XlmHHbTfuZsFWHlqa8tmdq7Q8IgZzFWiAUBTrlmvDBGxZ11mvAmGYgOVL2EkhK/lR104tNIIhofBXv1fI504dCHrCzwiCELR6lPnkk0/AGMORRx6J0tLSnNvkizu+4447UFJSgt27d2Pu3Lmt7QrRnQmGgNIqoKRC1vXTBvFgREyksQrxZwa8g7zHWsXdzJyAcC2OVQgh3MkKrZXIkMIxN4NAKAyYAVEio7SPOE64xLetXJrUVYNVDYIxdCzYsHEwho8DK60C1v8b2LAahmmAjT0EgdNmwajoJ8o56dpdxz1Oj5VmYuGiFjmGFIIDQW/CrWBEZCjNFytNeNFLovgTmRBtRzohFV2awoeuN0G0PbblliwEAMgQolSjsBKreSEY1uYJ7noShaIykaB8PhkT7/XnVbnsqvlJF1CDEWFdBuDULfaguXKHIrKkomxTn5Nt252PGZPhLyVuiSlAJv6KwFMrWZ0bkRuVfFSF96jM1bEKkaiL1g1tgx7C11F/RIfSaktydXU1AGDUqFHehgNu042NjYjFYln7lpWV4YQTTsBLL72E559/HjfccENru0P0FIJh1y0418BgBmUpC2l55baWptPIntgNQ0zK4EBdtdaQyKbpHMEMAOFS1/oViggBXs+cnagHrDRYVFMKxcrBIqVgR34H9rLXAWaAjf2W0weeygB9+4JJDTtjDFyvk6us3tEyGcMl460ZE+epu5rTQNk8zBBgywUduZK1H6mE972KPyQIom3JpLxzUqREJmqSHjOMiXEvIsPeKgdKRStzS//E9HkFcHJ8qGRPgDdhFudSIDbF/ByvdRW/zNZKUDHNyC2F8kDIVXw7mZUNoKK/1obhjhmhKByhXgl00Qo3kZduZSayYUwo+NXvR9eKIFpEq1cwykocCnnjHPWY5K1bt2L06NE59x88eDAA4Msvv2xtV4ieRlMDu+NODTlpNrGv2t4MARm1oBcu1hwA4wCXAqx3P61tBiGgG6Ys4cRk04ZoYPBeYMdOA/9yHZCoB48KSzSLRsB88ZksnwCR63NyWW05wbD0NuBuBlii7VEukep1tJwsFgTRHujJlfRkWJ6QoRzb+PHPK0qglQm/wG23jq5yt9YNHloGa49Hl7L66p/lmsMYQ85EUXm3JWGvaOh6tT+G0bFrM1oHdjitvuJ9+vQBANTX13s+1+OLV69enXf/zZs3AwB27drV2q4QRHHobs/awsGpRNzExMIYAzODQCoFZmWysl6zqgEwDjoadsMeWXZDywZKdA5mwJu0i2h7VOb0oHSvpAmdINqJHFUcssLaWlBeMxkXlmTbEvNkMAKEY+DhEpFwOhh2Q530sTQQyhbKGKMxgCCIbk2rR7Bx48aBc44vvvjC8/mECROc1/Pnz8+57549e7B06VIAQFVVVWu7QhBF4ojD3krLnAPx2qaXFlLgZZBJUmwVqyw070xq4Y2+g2S5jbr85RgIoqfAZFxkOEYWZIJoT/TySpqArGY2rrZpSbs64agQjGu2AA27gT3fgDtJL+U2+RSPuUo3EURPgmKSezytXslMnDgRALBq1SrP50cccQT69esHzjkeeeQRvPPOO57vOee46qqrUFNTA8YYjjjiiNZ2hSCKgsuYZc65U3KJq6ygqXiTpSV4MgG+c5t4DQZRjcIC37gG3LLc0OhQFKyiP1jFgOI06jmtAQRBEAShYRhC8ZpOioR5Ks7YssRcxu28CVMLwpjcn7sVH9IpGWZkiuM4lSe0uGB98e7USrYpcR9BEN2aVgfoTZkyBb/73e+wa9cu/POf/8Shhx4qGg4EcMUVV+DWW29FKpXClClTcMopp+DAAw9EPB7Hyy+/jPXr1zvtXH755a3tCkEUR6rRSU7CmQGmBFgzAJRUZsUO++FfrAZ/+x9iwcABhCNg3zoObPBI8JptYFUDxGLDDLph0uHsxHUe1GIHEAk3KOERQRAEkQu9PjHnQCYFHox4SycGw25W6SLhSVXWSSXrsp22mBkE1y3DejyxrSXuyqTcrNlRNzcNQRBEd6PVK/HJkyejT58+qKmpwWOPPeYIyQDwP//zP5g/fz5WrlwJ27bx0ksv4aWXXspq48ILL8Spp57a2q4QRHEEQm4Gz3QSMGStZMbAA1om63wMGC7+V3Unx+wPBEPg6TRYVX8AECWfbAtctsOaUuqnE97XZu5yagRBEEQvxwxmvWeMCddoVe7JDOXctSCZlGgHcJN1GYbIwQHAKSGlXutzZSgmlMaJuBtu4a/HSxA9CWZ0bGgRhTF1OK0WkgOBAFasWIGGhgZEo1HPd5FIBAsXLsSPf/xjPPXUU1nuP7FYDP/1X/+F2bNnt7YbRE/CyojaiUZA1Ai20lKYDYhBwk6LOoqqTBLnAGzxvRkULl7KGmxb4jOl2TYD4rtAWCwCclhs+Z5vwPdUI/PRMqQ3bUb4pFNhDh8OXrcLfOcW2NXVMI+eClRvBy+vAAtFxOIiUiL6wxhYKApYGeHSDYhjci7cuTNpIWCHY2IRYaW9pS1sG0g0uC5vnAtXN8a0JGFcnFco2jXiVPy/WXti26LUiaoF2RXOnyiMbYlnmDFZCoaLklFclqVRtdAJgmgazrUFM3OsyQiG5fwgk2ZZaTnfWKIaQzopyyuGwTIpcNsCko1iTq0cBJhBmVvDzUzNuA2u5lUALBiR5aR8ymTDFMeLyfrHhunOX/p2tiVdxE0531li3rDSYh5RHlpQc6KsTKCU0oCYYwJhGvubgnNxreU9gHAMCIbBk41AuhEIyqobtg0EAu46KRiha0sQaAMhGQCGDRuW97vKyko88cQT+P3vf4+33noLW7ZsgWEY2HvvvTF58mRUVla2RReInoJtAckG+SYF2GGR+ErG/sIMujFPlub6xW0RO6ULaGo7ve5jOiGE1lBETPp6HDDn0u05CYRCQDiK8LcnwRw6BOAczDDAazbDiJaA9R0I9B0I1O12Xd+cxAru5MJsSyz+Y+XgjbVisuI2AA5WuxOo6AeeahTt6y5utjpHSzRnaTUx1YJB1cqMlLTqkrca/2/GYtmWDoW0sItSXIHmZz/lHEjWe38zslZ0bTgXWd6V6ybnUjFke5/RkkrSlBNEU6iEXUbAfabSScBOi5lHn4MSDd7yTcwAaquBUBjcCIj5yDSBtAV8vRoYNBpIJx1rMrMz4r1SLhuGUMw21mb3K5106zSDifnVzog5TM1RMjmmkzLTCW1KifnbDIhzY0zLDWK5CmK1n5UWL2nsL0yywVXC25ZIvBarEL8fY46bPgPkb6eub4Zc5Yuho5NpkeKiw+kw1f2QIUNwwQUXdNThiO6KP2lWSnPd8rt3OfvIyd9s5gKbmUKoVkI0A1g4Ch6KAA27ETzqBBmvLAW6in4wDpkMa/Uy2NU7wErLgb5DgF3bRRuGKeslM6GtT6eEdjZWAZ6Ki9KRZgDMzojYrnAUPNko1zRyYaOfn3R1U+/FV0zMY8rq1hUSo9iW972VyS0k63HXAJAGEClrmaCsH0tZGGgC6XhUcp9c196JX+Te5zqTBiDfO4ofyBu8HftKED2BdFKM+8GQ9OCRCScDWok73WlPPl9OSFFJhRNixIMhMDBR5ikQAkJRodi1bbBw1FXWigaEJVKN92pe5jYQjPrmAWnNVgkxpXKXWxaYt6aEuwczXEWxal+fC5l2XkroIwqjLMOMuZ4BiQanHCI3AyJ5qXPd5f90bQkCQAcKyfmYP38+ampqAIjYZKKXo1yqde23Ql9E6wO7bmEtZpWttjcDQFTE/vJUI5gU4FRsFzMDrqCqdg1FYR5wLMAYrI+WIzDucGDIPvmPZVvgjXXeyd8IgJnuAsJJHqYLDDmR5+cRogsnGesc8vwGVjr7MzsDGM2InWNMus+rtrjwNGCGsFaQJbLj0JUeoZjXi6OxXrMqQVq9pAcBePajagbotyOIYtA9owzTnTfyKpD1eRLe+Uy5O8MAoqXC9bak0nssXQ8b3yP+V0pQ5dqdbkTWuO/MZcKThGeScKpKBIRbtxLQuB5SlG8OZAZgSAuznTtUisiBugccY4CMKzcMMCsDzpj85YpcPxFEL6LTR5n//d//xccffwyAhGQCYgCPlIqajLlKMSn3Yz2zppoEcs2tukCpv/e95ukkYFtghgnObfCNnwJjv6VZr1UzMpEJgMC3Dm/ydHg6IWovR8vADKEpd+KUpWY/a1rSLaO6lc1Ki4zZnrIbXWBS8y9W8i1elCudjtGCISgUBXhYtKXqT3NbWCiD4ea3R7QMPcNuOuEKySpmXMfOiIWZY4XSzEKBkMjoTp4ABNE0ZkATlPVJjwFREdbD7Iw7h8iSh2ITprlkwzu/2JbYV82tKu+DjnJ5NgK+Ocr/7HL3c+U6LUOKOLjwpDIDzv66kVi4Vlv5FcDMEMVLVQw2kZ9ASPyOnAMwxDVVpbwAgBlgXCpJVEgb0Qw6eg1GP1BH0+lCMiAED0YLJEJhpeVYoARUU7y3bbhpogtZXeWU6xeIc1mmFfW7Ya9dBtZvGFBSATZsH5Gh2gx4BFv1mss2m7xrk40At0Q8s20D3AYzA1lJ7DgAJttl0tLG1bLBlrHSwVD2c2J2oCVZJV8yDG/SFGYIt2kr7SZGy0UgLH5L3oqYZEAe1wAsnyDWkraIlqMrcfTFtP85Uwt12/I9f5bMlCsXxWQZIoimCUWlwtH2lnwCxNhr2+BMS2rJbeGppKy2gPscKqWVLec6bovJiKt8HZYUSpWgrXIIJIGSKpkQSjM1K4W1Jcd4Jj5zvaS5mM+ChrO9LmI7CmRoMcg5w608YjWRj1BU3BOZlPit/Mk/1VzquZ6aAYIgejm0KiG6FumkiK8CZBxNAM7gbZraxOl3tYY7eUZKXe1pLhffHLBACDwRB+s/TGaczjgLdw5kC6fcFqUuYhWFGzZMqCWAEoKdxFVci8uUSwWmNPCOkkAeKxDM1upz3nHrBM7dBZkyBge1pCmGARhNaPUZk9bGNsh+nU56f9tgJH+yMKJ9CJe4FmPdomMwd3ENQCy6DJ9nCBdKE9sCYMusuOXkck0QTaHcZbkNxNVzxgGmxj8tJAdMTi+a67OIQnYUvd5M2Tkwg2J8T8b1TrgKrlTGuy1jMhZWhVUwIF4nFNwqLEZXpDqeYNwdJ/wxsjmNKDRWFIWVdrOJc/W7qd9GU1HoygiDjFZFQYm7ejw0yhBdA86F62wq7v084NPj5Boj/Mk+VAbOcEwsxPXt9D99ou4zGGyvA4SwZUurcTDsCMdci+1ijInYrWJcvUoqZP1Iobll8lwZt+VrSIt0thu4slwzv3VWVwp0WIINnzRu53CF70j8rviBFtQEJbw4SXaK/G3VcxaKehfZzACi5XIbU3gZMLm9svYHo9keBwXj8QmC8MAMNwOxll8DZsCdO5TlWFvMqwReDK53FDNMMQcx5ZIbcpWakRIxl5pBOO6lSiFpBtyQGSMotgvHNK8emdOjsr8YWzIZeSztNDSPJBYp9Qoe+mt/zDV5DhWGcze7tRmQf0F33PX+CN79KHEXQQAgIZnoKiQbhJDsWSgzIeSqSdhTrinPQl4vCcF54ezPejws52D9hsiagimxfNCF1Vwm2yIsl4wZYOX9vLFfmosqcyRl7X+9NI7TDnMEbG88WQcJFszwnm9nx4LpQjHVSm49nIuyIPE9QP0uNytqSwlFhaDMuSzhph2HMWGBzqTgrNTMHJ4SBEEUhsv6tiruF/65ylXMQn7r4lPMMiae2UiZeD6VBVLNtYGAGGvVn2xfhM/A/R8QimElmIViYKEo2IC9wGJljgDGtH/BbTB1TD2G2h+2oX/WknwWvQXOgYZd2bkhHMW6/tv7vu8quU4IogtAowzR+XDuJgFyEkqYwm3aMIRmumEXsgZuv2bZDHnjGm2rsEZUn9RTjZpgwF1htoA7WlFx9NwWgkdW3JZyI9cnJOkEx4zcrtX+0hkd7eoTjqFgyZ+OxAxIa2UTroJEcdiWVzBOJ1sXI5xscN3zmSldKHP8ToEQEArTb0gQzYXb3pCTVBKIKmuvrow14cbIaO613BaxwwE9UzWEYKXmK86BVEJYk/XxQR03k/YqrjNpUZoqGHYFacbE56q2spqXI2WyjnLGbSPVmK0kVhm8VX/UXGinAZAHUU7sjPiNDDO3vGsGAUsmf9ODwhVBuq5FQe7WPR4SkomugYpFsm0xuSq3K8B1jdZjcHMNFn73K/97n1ba+mYr6h68H8ExoxGdeChYVV/te4hj2Ra4bIf5MmRzANjxpZhwwlERsyUTZTDbEgsFNenrDYfC2QK5ateyXRdzXUh3ZjJNeAc6PtlRVxJmSOPddviflZbeV5yL5yAVh3u/2gCkW2ZWxlxSchBEc+GcAw17AGhJT7kNnowL6690uZYbi/lGT3wVimYrwvxxwO7R3HZ0K3U6IdrQ51UlYKeTMmt9THyebNDaU8pgCGFZZdPmti9zd9ZJwxlP/OFShBcmc6HYlvQ2iHqNAszwOqHpv6FpesPUCKIXQ6sTovNhTCTtCUXEpKoLyM42so6wimuMlGS7O/snTWaI5EJZFmgbPJOCURJF+WWXIT5/PjJ1Dd5tzAAQDEqXbZ9rt2GKTNW7tgPgQCYB1FULgbi+RtSETDWC1+8C9yQhgXBDC5d6z10lYjFkAhb9O2/H4VhxAyERP02TGdEWMEMkoQtGxLPVnBjvdEJYje2MWOCmE4CKbwRcq384BgQi3n0pozVBNJ9Eg5hrAKfaAjdM8G2fixrlsh4xAPcZU+9DMSEk+1GVGxrrwZONsmpQAAADTyVEaUR9e8fqq82P3BY1k60UkNRrpeveTwYQq0RWBuVgxC0Fp5evCpe4SnI1FwejNPcVwjCA0irxW0fKRLx6tExcs1BUKPCVAM1t1+OO2248OlEErBP+iI6EVihE18AwpUCbh3DMrYmryhoYATfLcSCYW7NsBsSiP9XoZMbmDXVAKCzKYpSWot899yH18UfgNTsA2wYbNNydsINRMFWz2QgCwTC4bQG7tgIV/YXVmDGhwbcywn3NttwSGSo7NrfdJEeAUASkGuFk+1SWdF4va0TKsgyGIc5BaewN0y0BQhBtiYohbA6pBNC4R7xOxl3LkePpEBLCtxKYIzEgbQhh2ghQRnKCaAmphPi/sV54MZlBoWSO14KnU2JuC4aBsr7iO26L+SmszUGxCiFsK+VzKArUVwM1W8T3wbDworJSQH29CBuqGAge8FmpdY8v8cLtZyYlFW+lQmgGxLxqBsS+gbA7f6uygpEyMZYwuAKyGQDQIM4hGPHmHiFyYwal+72EmeL3V8TKhYVf5Rux0mJMpiSYBOFAK22ie2DIGGUdxuRk2cSE6dvX/mABjAnHAYGAyFJtmAiOGQWWjIPXxcGqBnn3rejvba+uBoDMOg3p+haMgIWYsBxnMmChCLhpanHLpld770l+os7HEBNXLoK0KCC6IP4Sa8m46/UBLizIfnfqYITuZ4JoDdwWNYWtNBBPA+X9AWaAVQ4EK6kAANi1NWBVg8X2ueaVYDg7AaNe5ikQcusXB0NCQN21Feg7VAjVtu26cNsZdz7zJMtUyjIzu1xivvnbDGT3l7Hs+Z9oHbnWIETzoJjkHg+5WxO9DtZ/GOztX4JLAZlbGSAprNSsrExYigsRKRGTuOaGrYRhxgzHGsdsnxsaQfQ0nEW2NnlzSyyUbatwdnmCIFpGJqXFIgPIpMUTKIVLzjlYWVXz241VAlWDgT5DvUkv5fwo4ltDYjsjICzRiQYhKEfLZZZ6U2xjBkBLTIIgujNFW5JvueWWdunAtm3b2qVdohti20BjnazrJ2s1cpV1l4GDC3fmRL0MzwgIN61EvVgoREsBbsPe/gVYrBywMuCb14PX7gb/+COwcQeADRgkXJcr+4DX7xaLiXTCLZsRCANgMsZSlkowAzJuR2ThZGDCrdROAxnhwg3D9OSq5swQ/bYtsXgxDOGupmCGq8lNNQr3bFPGQHNb9EMJGE4yEwMwmHM9YJrZNWq7Cumk5iIu3bn0chRWRghYKgaN6J4EQkBpH6B+N7yZYCRUpoUg2hSeTgoX6GAUbjUEgNfXOGMpM0SYj7X6Axj9hwKxCrBICXg6Cb55HWBnwPoNA4tVuGFImTQQrwUiJbKWsgkOQ9bazYjnPBwDszNucj5DCsSGKVzAUwknCSCHASRqwb9eC9Z/CJgZFOFHnIPX1QCpBFjVIDBDzr7pJJBMArFSMOl6ze2MmB9lfV+mkjWaATn30dxRENtyqwyouTZR752XU6LMFzcMkYXcygDl/cECFApDEEWvYG666abiSt4QREtJNriCVEZLJsFkQisV/xSOyYE/5ZaIMlStSAZj8N5AMi6E1n5DYb/xEswpZwFDRwB1NWBVA8GTcfDtm6RrNXPjdQaMBJO1kkWMMxdCu2MJ1uKvzJCr0ee2EJ6l+w1Tk5DqtxHwZrHmtpsd1LbEdnpWz5TmtupkwrYA23GAAyxZOitXEpbOgNuuYKydC/eV4hJKA1P83maw82sudwc4F8+GiuPzZ6PuTMygSBKjsuUyyFIwEcpASxBtzc6vACsDFooJJa9MuCWUywGhoJVrNWP4OLdUVKIBfOvnjrKW79wMNli4OnNAbJeMC08pCbMzYtoKy7rHyr061ejmyFDClJUSc5URFLI7Y+ChCIzBowA7I48h+svK+8Ku3iqjMkQtZ24GwWKqrbQQ2mQok5h/VXULea5pg2KTmyJR764fGuuEYt3KuDHhGfmbmabwfDMDQgmz5xvhVk8UpqNzaZEI1uE0S83vZEskiPbAf3/lqt8HuIkmzABYOCbmTc1qyzkHwlEwDqDPQJgXXAM2eITYd+AIAAyGYcDetBqscqBYGASCQOVAEaOsBLx897tTlgkiTsopsxAW+zKp6c6kpYCdcTXeWeeosl+rNrn3O91dTVmM9WQpXemZTDRo7rXeH04T7X10of53ZZRCBRD3VbSs860oKhYRTMb9a4n3KPMsQbQPtgWkE+CNdW6ZJTWHKOUU4ArPCi6VldFSwAyD9Rvia1gkoGR+QTkcEYI34CqL8409ytPJDMqRXeYm4Dx7Lo/EnOPmmwc88waX7bsnlLsPhEvWeiPH76CU9oC4pGZAeOwRBFG8kPztb3+bLMlE+xIIaomAuNRI67X9VNwvExO9tFIxwDMZMFUiCRCCdEmlJlSKf7htgw3bB6xcq42sMAIAS2d/rvrg1JqMuVp0ha7ZDkWF4KhqROYSbv3PlK+Wc85t9PctTYCkBPK2dNX2xHILDQeX2n+eahT1oZl0S7fS4IyBGeTSVRT6oqWrxLenGt3nNRhu32Rc3AaUpwZB9GbK+wI1W8XzF4mJeZAxIBAEMwwxtOtzpZpSbBus/3AwfY7iuijLYQzcy3Mo9a0n/pkxWVlCKoRVZQbAkwjKL84yWbWBc4BxDqPETeTFOQeTcz3nHMwU1R04hJWZKY8UNYcygxRxxRAukV533K2i0VjrXscsoZmDBYLghSqNEBpkSu7pFC0kL1q0qB27QRDQ3IoBgAlrGbedmBohDEu3K12441y4EWU0LbcuzOomaWdSz6FRzdkPwF1laG3obmaFCEWkC7KdWyDVBWfVdrGEoi1zu5WudwCEcNNW7tqG6bUkMwbYHGjYLSzsyTgQKQUzRUkP8XtmAJBLbpMEw0BSXttAF6ljqWe21ixYbU4yLmsvQ4wJVDaK6MUwZgBOBQYGlPUBSydFiA58Q4Nj+RVxy8zwV1RgHkFZ+wIwDJ/8xEV5JjXvxcrFZyrmNS9C0FUuvoxbWWEYTM3L4FIJXibcf/1tF6oAQWQTkKEwOqr8pKOoN12PNrlGYZwsyQQBUAkooivhj19M1Luxwc7MLwd2v3AJuHE2nGtWTe5YLwF4vSHy1YT1uyiFZMKLlFYeo1hPL8MESiuFJTCTcoVIjxAP72fwCfacSyu7T3hPJ4VGv7mCckpLoKViiNsiCYpyB9Zi1xgAHi0VQpQREMlAHMUFxARNNE0gCJhyUdqZidpsW3hGqEWVelaaew9aaZExl8GthZoLzl0BGRBJZqIkJBO9GDME2DLbtBJYs+Yy6ULrTnxiTmQsTwiTsohxtz3H2mi7nkfphPgLRaVbrp6vIw+2ns9Dzms5ldRqLDHhhHBkIftAJeSKw8q4nmzhmJg7dAFZIZUo6jeQ6UkJotdDQjLRdZBxxc7kaMlMiyqBh0JN9PoigHNXbnUEULlw19vU28myGEsCASClfRcIZi8EmjODMAMIqMlJs3aHoqIPukWOGfAkKlP9zdVXlfwrHMv+rhCGAdhwhWIrDVjB4izjBeCAUGwYAZEhFfIYHKJtZggXawAeBQBRHM5CphNJN7qKHlsueBnzlEMrimRc3t/ydbQs/7atEcYJoqcRKZFzkkyc1VgvnotImZjr1JyiPKqU5wnn4jXnIoyJQ1ZUsMWf3QB3XFYxqnpYkBa7mowLi25WDfSwK4jZtjuHOS7hISHgplNujLTCDMpEjiFXqFfoCuV00sl2TTRBKu79zZihXVfm0aEoOCCUkeRx3TRUJ7nHQ0Iy0U3Qk3sw78dgAPcl/2CGZ0GtYq6cd+lEfotcICya4pZ47WjZNVoyQevxYYC0uBpeIVn13XErb2JQbMmgGYrCTbrUhijLP+QCKFwiYsoCouwHmNHpMh7RRqjFs51xM8u3B4yJ+quphKtYIojeDGNCGOUcqN8Fz6QSjvlyQyCPl5CuEDVFW7qnVJNoFmel7DUDbkxySnp/iIBidzfDFH/hqJgrUo1akzlCqUgoaB+cJKE58p6k0+K+clz6CaL3Qmp5ousQKXEX3Ibh1vFTAq8hYllhGJrF1R3kGQCkU+Br/+VoTxlzk44wzsFlvWNmmCI2JxeMiVjicInrxsYMoQFXSUtaUrZIuTupfiekBUBp+lVMUCiSXf84EMoW6g2zZf1QZbRMeVyZKbzV6NbuTAo8lRAZxjkHY4aMO/NZkGkR1L0Iavel7l3RXOFVPQuqhFshVOZsJ4svQRBZ1RCUcByKuPNoqMhnhjFZR1fOTwFtbsipBNPeqwzbWtIuUZbRdLdTFSn0ZFvKcqyje6QYpjbnmm4/gmGyIhdLSCV2k+NsWHsPaUvmtmv1t20wgwF9Bndyx7sJDNrarSP+OvuEex9kSSa6DoYpslarGBpAaJz9llYdLQM05wzYvQM8vhvY8hnY0LFubBUAWBZYJiXcuQCRbbM5BMOtq+mrJn09xtK28wsJhVxQWwtj4tqiDS1z4aibEMwIgBmG5/cBoAnH8rOukqmZKA7l1tm4B2ABN5lPcxetZhCIUWwxQbQYwxDzUToJQPOyYIaYR5tLIOQVdP3olt+m6hObgaYTbCllbSohasAD2fNra+fc3o4ZEJ44OjGZVTy+R9TbVq742lzMPC5vBNF7ISGZ6BqkE0CiwZNgiwNAvE4Uug8EnZqPyjLMZbwTkzHLzLbABwyDMXA4EK8D37oBbOgYOOo32wZnBphlgUuLcJZiLpNyEwoZpoyJVvHMWpwWlyUVCi0q1D6JBq8LnC4wJuuBpL6D1N7bGY83VJZ7lHJLi5Q2T0BJJ11X83BJcTGenAvFhW3JmLGIKCNhpaWlwRBdSzVq52aLUh55rRhyYja6+BBkZWRcF8QitJVx2z0C24LHTY9nQFMJQXQQ6SRQVy3Gz9I+QEmV+LxhtxA4Q2HwkipgxxdA7U5hTawaKMpDqfAe25IVBqRnlEqumKgXz3SsQghYtg3Ed4txMBxzBa50AqivkV4ecg6yLTHXcRsIRcWxtq4XQm6sDEx6LHErI+JjAyKumAXC0sPLAnZ8CYQiYNFSADIuOZN2vceCYRlv3Yz5q7fDbXEfpFNAOAZuyLhkZoAxQ8zThgzxUmuJtvAs6xXkCMVr9+MRHQk9CUTnw7mcXF0hmKvyD+mkmGyjpUBjPXiiDijp42qhrTTsXd8A0VKw0ipRXggASipEWSgn0zUDN00wC4ByMUrUS2uqRrJRfg/XfVgJuP5M1IkGoLQJITmdyo4Rc5JV5dDWemKFZT+ckla+fZzEXUVk2FBZgjMp375NuLrq2YwBsUDJpITmX8Vr27Y7wWpJQWBb4KkEEC0Hi5QIYVNdU8akBbKLL3LSCff3SjX2PiGZ2+4zEYy44Q465PpIEB1Hwy53nK2vAfoOFc+osvKmEgB2A3t2iPfSc0rlhOBGQL5WildZximTBgw5zyTqgZJKd+w3TNF+MCKef3Us2xLHi5S4pQ4B8X2jaJNV9gcARxBmjAGREiEscw5kkmLOZ4aYjxvrwMNRUeqKMdF/W2bTV15lLU1a2duwLVehEopI12pX2c4Bd13jjOOMEmoShISEZKLroYTlaJmQEZV7UFAO8ulGMYEHREZmNmC4mOD9SjYVc2xKbSkgNNCZVH4LsC//l/s582bYVJ81RVPb6MZh5wP/NoUmrCI1i0q4LdQ3W5apMgPSmm2JxZLvvHkm6R5VZhVngLC0ZlLid4qWu0JluMSNeUsnxTkGwl1fQAbgub69UYmbbHQXpnaD+F2V9Ui/VwiC6BhUfC8AT7UE/zYKNX9oFSCYqqCgVxlQSmUGOPOQXiWCmcg5P+Wb4wyxvfIAyzt+MqZtw7xKX/3zrPmKQnWaJF4rrqFK9CaV2kzNvXpVEAUDhUERhISEZKJrYGUAJ8kWA+M2OESyLse92raFyxYDVCkDxgxwwwRCcjtVC1LV+dOTdqm3qlZvIEesU7hExtX6po6WCMhiQ99bX2yu/lo/hlOj0ndc3ZqsknwVg1/QVvHRCtsWLtQAkIa4Dsm4cyxn92QcSCfAwzHNDY7J2CYDiFbI0iAsu2+5PuvqhKLSasJ7Z23OXPcpIIVjmj4IosMJxVzFlbL+BcMih0WqEQhFhYK5/wigYY8oaejPDSEFU2eO9CdS1N1uuR4qZLiuzqlGsZ0a00MRN7Y1GBH9SSeFRTlWBmaIMYNnUsL6bJjCSmwGXSVuMAIEZUgVMwFYrhXZT29UWjYX6VatX0MhIOuVQHJdSLq4RZFLedPexyM6FFrlEF0Dlb3aysCJt+U2kEyAGyZYKAIGGygpF5OslQYLBAFwqRWXMcqGmXt4ZwxMq7XKgNyLfFPGZ+kW1HyW3GLcTHkTrtYMgCVLX4C5bfoXNdp5uNI+ZGxZERbZQEgsQrgtYtD8mYL9LuFWGn5FAWMMPBSRbnUAgw2eEtZpbgZExnBuiWOwHuKCaxjClbC3Eoq4SqNcpWQoSzlBdCxcExp1S3Ks3E2WxTlYMAxUDtA8dpgnIZMnX4RULIv2mBDEASG0WmnxuaqwwLn0SpKhQZYFBKTwrI2VDAAG7uXru8gdglDEPQcrI+fjoE8hLOckM5DbskkewU0TDLu/lac6hnSZY9CrJbtfkwKUIACQkEx0FdSgrIREwwBsAGVVblIJZ9sgEFS3rtRcqwnAtuWiII/ftEcwzADIkzlTt+DmEpSdWOemzisoXYzztAPW/DhX/RzSqeLcXRkTmn2/ZdrpZ8BVTADCym5b3nPkHMwwwWPlQuHAbTAzAG4YbvwYICZlqmfbM9Cz1PrvGz3bbThGbtcE0REEQ24d4nxhQ0qw9Shyi5AqoxVe65hhuMm6HMWsreXNAGClip/Dcs6bTfSL28JybXMgrdVyJr1c0+iVQeTc7ybUVBdQV9y7iguiGChxV0+HhGSi62D74quYzy3IcZlmjsDJZZIJJrXOzFkUaC7Mqk2/21a+zMpKqNX3dUptILeQmQ9VgiGTdGOC9SRcjqZXQ5VG0i3G+Whu7FC+9hgT/bQzbh1FMwTYYjHmqBxUZm/GhDs8kxpqvV3SQvcs8t0zaqGuXkdJSCaIdicYEQop6TmVE30ezDWP2JbYxgwJIReAU7fcTy5vJl3h25wKBf59/d/lU+KCAQFThAIpmqos0dvxK+e5DcDI9iDI6SlHZnqCAEhIJroKSkDWBmwmtZ4qqQevrQaLlYMHI+52mZRIOAIje8A3TFfjzblM9BUWAqth5Ld8+SdpQ+6nC88eTWwTMOQQkOEudvxCMlcaXZ+VPBdtmVmYMXfxlUqIBGlK1yDjjlXMmycRGuAqodU5ET2LTNot+aWsRp5YxnZKwmalxX0ny8cQBIGmx33DFGWc0knxnBqmEGattHBjDmj1hy3TnR+LgTEgUi7mrULzaN59y0RmalVKToUCQc6rKoGYbgU1mBx/ZFkqdT5EbmxbVJNwjAmQngUGmL6mcNYaEo+BgiAIGmWIzkcTbLmKHVYZL9Ugn0qC76kGK6kQE2wwDKQSYKGwEKRhARkLPBh2NaVq4gWAYFRM5inprmVbYpI1crlb+5JrpRtlLWDNsuzULS7m/HKcq6o9HC33CvZWxiuE5BLEPZrfNna/4VwIwlYaMmBJusjZQPUWcd3K+wGRmFio6NdB1eBUwhTR/eDczQugYgTTSdetOpMCWKlYrIZjQpnCWPskNVP1VNVxm1sTnCB6M7mS65k5np+WWGSNZiSN1LFlIi7Hsgkxvzp9CbsJwNLS+ysYEnHPKW07SwnKecKlej3c+4rbMolaHXi0HDAAqDrUZsBTraLDk1F1ZyhxV4+HhGSi88lyweLyY1dQNiIx8JH7AXW7ZKZMAzwcFXGxauAI+BJ/gLmCpp3xxlEBQpgL5phkrbTX7YvDFRygDYrFlqAwDGmJTng/4wxIJb3XIRDMThjmSWbic0fzn1Mr4JwDjXXSdV07T3mNeeUAoGaLSLSiL778WmfbBkiWaR80d/c2nzA5F0JpRt6TgZBQJOn3LeAubg2zfZOa+UMJlIsoQRDdD1VSsKltADG26XktkvHsbalMUX4MEwiEwdMJR/HOAiFw2wYyKTF/MyaMEv59lVKcIAiQTwXR+TAGvnunp5yQnjjCE0MTisis1u6+qm4kdyyb8Fk4mylM6BOEckfyWHclxdb5VVrzHGU4nPhjHaZZrXX8AkLe+K3mw7kNbF4nBCTdsm9ZTswxC4bBBuwFNmCEV4j395WsyO0D52KRmahzs6+3ZduNdV6BWE/M5cA6zu3Z1EvXNNOtkyCIroXuPq2jK1mD+azauZJN0niQF5VYk6nxWlw/ZhgyS7l8z5jXH85ZT1BMclGoNVxH/hEdCgnJRKfDrQzs155E5oWHwL/6XAi7hgluZYCdW4WgpoTVUMR5zxgDT6fB5STL1D+25RVKdfTP8rmZqWRalqy5HAxL11Pf41Js3A63s7NMclv8BUNuJlKnX4HcFrNAGCJOWRP+m+sql0mJesiJeu8xG+vFZ8lGp888nQLie7z7hyLivMOl7meeRCAmDeTthX6f+O+p1mJb2e1x7hXEDVPWwO6gaYMZIn4xUir+6L4iiO6LJ4aYuc+1+ouW5Rd8/fHSkVLKUVCIfPODXHs4eV44d0tjqu8BUkAQhIRGGaLzMUwgVgpjnwlAJAxesw28tAr4Yh3Y2IOENlSzDKv33LbAwjlKDSnNKbeRUyOqyh3lmwiCYRFvqQQSOyMsrI77sXKHLtIlKUuo0Pqkskrrybts2e+sBGJSaFBWdtNs3mSm4o0BkbwlnXRd2pSwXVcjEqMFAmCmCZRUgNftBsJRsGBQWBoDIZkBm3kFKcZEu1aaMo+2By1V0jS3bb+bv22J37MzBFXG0GNqbhNEb8YMiDFEJeLTPbGaesbNgCwDZWXvS2Qjx2mVYJNnUuBmwMn3wrgNcFk3Wx/7leIhVxgakYM8eWPa9XhER0JCMtHpMMbATpwJbN/oWi4b64DKPtAHBQafyOufWJUgzS03FjmX67JtAbCAjJl7MkgnhKCnrLmqtmNL3ZsZExN8Y52TYdLTl7QvLhk5hE9FrmQsbQALRcD7DhMX2a+1L62QL2R/rIy7aFFCdzE1o4nWYQaEUkNd/7a8D1Q91HQSABdKG6fES5AsuQRBtB7pJdYi2mnu65EwQ4zZ9buEMSEYdqzH7ijuy2wNuPN4KtG++SYIoptA6jiiU+CcC+1mshF2wx7wN58Cr9mmbWADiVog0SDeMwbODHdIZ8zrWp1JewW1fPGynk7ksQQ7FmRbE1RlzeaWxoGaAdGunt0zGEG2ZpDlzhoajLQ+aZFKhqJKgmgKAp5KwF7zAQDmxIBzyFhlfy9TcZnxOO4mRDNk/KgZJFet9iQQEl4O7WGpDwSBqHR91Nu3024yL0KgsoC3dfIgzsXz1Jbx5m2JbbkKQ/W/uhaZdO7roW/bHNR4mW+/lrbb1VHlj0jx2OnwTAo8nQS3MuCpBHh3+k3MoJinbeFezfwxrflC0tR3BEGQJZnoeDjnQO0OINUohLCabSI29+Pl4MdOBQsEnAyLfNsGYNAosJJKZwHJmQFmSNehTEoIncwQQh8zpCXZcBWlhukO+swU36v6jLkIhIG0LB/FuZMp0o3zkRNLc8veREqFpdy2xOtQVLSfSYnPmCGEFGaItpWVNhRtO6EoEMrdVsNuwEqDb/0cGL6v+KyxVvwfLQcH9wrKfqUEuLBEEt0f5fmghwBk0u1T5qk7wrn7HIMBJRVtk3XbtuUzJ10go+Vdy3qfaBBl6wCZo0F9oYWhcFv0W3mjqGRz3IYTh1qMq6yVdhWkZjDbqmVbwjMHcnyOlHWta9Ua4nvcLNClfYRSjOhweO1OoHYnUN5PVNPIpIFEHXjfYWDdZSwsrRJrLR6S+UIMtyJGSCpb1b2mr5O6y/l1Moy5RoWOOh7RsZCQ3EX55ptvsGzZMixbtgzLly/H8uXLUV1dDQCYNWsWHn744c7tYAvg9bvAv/i3WDhV9ANTC59IGdjR3wE+Xw3+7gJg8jlASQmwe7vYz7LBDANMCY1mEDwQAsskxSCvXH9VbI3HLZp7Xa6dWOICmQLNAFBSqWla5bZmqUy4BRmK0kxHjGBY1BiGti+TC0d/WZ9gWC40OyijYVkfIBgBr94MvmcHjAOOcy0addVA5SChXFCCcTAEZLgbH00xyD0L5W2gfm/6fV08Vj4urDVtIchYWl13bntrpncF9DI8Vlrrm1Yaj0NcD/WdrVvb5XjSVG1b5aWiH4vb3vE2o10r2xLH6QkeLKoMmzqXRL14Dhv3uHNYuETMGUS7wDmH/cliYOMnYp2y72Hi+Q5GgFglEK8FKrqHEMkipeChmPv8+NdFABCrQM51EkEQJCR3VQYOHNjZXWgTOOdAfQ2QSQl3ahXruPsb8AEjhWYsJif8fQ4ABg6HMWaC2K9yoLA6lFSK74NhMZarEgaBkBj8TV99ZH+tZP9nxQi3+RIGMaN1uRNyHTtfav+OyiIMcU2Nb50E7NkBlFSCRUvBY+ViwRYIgQXDrtDMDBkfFvS+J3oOjInFUyYlFun0+wpruvKu0Gmta2ImLfIg5ErU15UwA27GXP8Yq7s8m9q46R/DmrK4c+4VkJ1j+a+Nr52elNzNf26Jem8yjmRcCGz0TLYPu78BPl8JREvB9j4IzDDBZIJLbpgA617XnRkGsiMr/eulHOskomk6uiwT/T4dTvd62nspI0aMwL777ovXXnuts7vSfOJ7xB/nQEk5EN8jSjulkmC7doJbGbD+g53NWT/xmjEGlPf1thXwWSBokdCmsGAY6DfMfW+YoiyH84HPRb0lJaiI7oMqf0YI6qvdGupKcaDi8FsKt4FkvfveVArAYNu4cLclsQopwMrn3kpnx2UHwl5XTcMUlk8rLfIWNHfMZqZwtfYvDtW4Y2Vktv0uplBoDSr/hf8zvVoDxYy2H/J5Ng48Rijw02kn6ZX4oos9lwRBtBskZXRRZs+ejYkTJ2LixIkYOHAgNm3ahFGjRnV2t5pPwx4naQQDYCeTQMMeGPscCkRiYIzB3v4lECsBko1gfYd2do8JgiCysX2CiRJkdUWRlYGIkw24gp1KQGWY2ZZVf9IpxrpuDKpheN18A0GRBVe3goci2QKtKeu+W2nxV0ipoMJPUo3iWoVj+T1q8uVX6M4wGePuWI/9QrG6pzqhb70EVt4HbMLx4t4zQ+K5T4vQLmYYPctrgSCIgpCQ3EW5+eabO7sLbQJvrAdMkVQrs24VMnf/HqyqLyK3flt8zzkQiQE7NwNlfYHKAZ3cY4IgiBwEgsI1GhDCmT9RXSrhJrYKhmWuAZmMi8u43Wi5rz6sIdrKpNzkg92JYEjEUtsy7CWfla2xzo1pjpYVLi9jBoFoD4gvbimBkEjYlU66yckcqZhlK1Z6IVxViGBMuhO3YdvcBqvsB6hqGkoRY2dEPH2omz2jRDtCdZJ7OiQkE+0K6zME9qp38cRvH8K7z7+J/mVhXDl5H/Rftwrm2PEAOPg3XwB1u2AcchJl7yMIomsSjgFG0n3tJ53QXieFkGzp5ZxyJK5SmcRDMfd9d0Jl4W6qhnyy0fuaarA2TUDm2vCUNrSFl0JXSujWwfCv18N+8UHxjI3eH2zswWCjv9V2wnI6JZ7bYDjbrT0UJXdrguhFkJBMtC+hKL5++S28+/ybGDhmFL535y2oGLc3EAmCM4DX7RbZIsv7kIBMEESn45SoS8SBWBlYmcyNEIqJmFuV+duPGXAFGsOUSb6aSDil6O5jX1P9DwTdkmK9WMBrFsrdPCnr0TNTeCGEY93/fmkF9vsLhNcGAGxYAwwaLko1tZUXmqoqYdvghqGtSxjlQSF8dHDiLrIkdzj0xBPtRt2OnXjjyh8j9ekKMAZ8/67bsPfh3xKTjpxsWNVAsPHHkps1QRCdDrdtYNdWV8CtTYKHomBKMCm0SA6XCCsft4FM0hWYlRuyGey9i+zSSmFBZkxY44imsTIiNtvOuOV7mMz+3RUTu3UUMVkTm3Ogsh9Yv2HiuWrKm6FImGGCVw0SdboN6U5rGCIhXU9KEEcQRJP00hm7d1Jb6y1fEg6HEQ63X3zNvTPOx+fvfQBwjr0HlKGisszRyqr/OQBW1qf7xeIRBNHz2L1NK/Mk44iLjQFlTCSu4lwIyZ7Pe7lgyAxysW42ue47siQZk86GHQwBtTUwJs8QCbXAhUKhjZLesUBQKHYIohBUAqrHQ2qxXsTw4cNRUVHh/N12223terzN//4U3ObgHIinLFTFdzvab55OCLdGwxQLS4IgiM4mnfS+j5U3f+HNmBZjbHhLIhFEsRgB4d5vmN7Y2GCk91qRAbBYGcyTvwfz7CvBgmHXHdrKdG7HCILocZAluRfx1VdfobzczcjanlZkADjpv/4T//fLXwGM4aQ770Bg0untejyCIIhWUdYHqNkiX/cDq+jfsnaCYfKOIVoHY8L6Thb43DAmlAjKa4OU7QRBtDEkJPciysvLPUJye3Pq//wch3/vHJihEKqGDumw4xIEQbQEFqsAD8cAzsF6Wg1eguhpREoAHoUTN0wQHQqVgOrpkJBMtCv9Ru3V2V0gCIIoGmZS9mWC6BYwJrJ+EwRBtAMkJBMEQRAEQRAEQRQLJe7q8ZB/CkEQBEEQBEEQBEFISEgmCIIgCIIgCIIgCAm5WxMEQRAEQRAEQRQL5e3q8ZAlmSAIgiAIgiAIgiAkZEnuorz77rtYv369837nzp3O6/Xr1+Phhx/2bH/RRRd1UM8IgiAIgiAIojdDpuSeDgnJXZQHHngAjzzySM7vlixZgiVLlng+IyGZIAiCIAiCIAii9ZCQTBAEQRAEQRAEUSxUAqrHQzHJXZSHH34YnPOi/wiCIAiCIAiCIIjWQ0IyQRAEQRAEQRAEQUjI3ZogCIIgCIIgCKJYyN26x0OWZIIgCIIgCIIgCIKQkCWZIAiCIAiCIAiiaKgEVE+HLMkEQRAEQRAEQRAEISEhmSAIgiAIgiAIgiAk5G5NEARBEARBEARRLAwdnLir4w5FCMiSTBAEQRAEQRAEQRASsiQTBEEQBEEQBEEUC5WA6vGQJZkgCIIgCIIgCIIgJCQkE+1KMpnETTfdhGQy2dldIboYdG8QhaD7gygE3R9EU9A90nzomjUH1gl/REfCOOe8sztBtC+1tbWoqKjAnj17UF5e3muOTXRt6N4gCkH3B1EIuj+IpqB7pPl01jXrTr+V09ctX3T8NRoysltco54CWZIJgiAIgiAIgiAIQkKJuwiCIAiCIAiCIIqFEnf1eEhI7gUoj/ra2toOP7Y6Zmccm+ja0L1BFILuD6IQdH8QTUH3SPPprGumjtedIkBr6+p69PEIiknuFXz99dcYPnx4Z3eDIAiCIAiCIHLy1VdfYdiwYZ3djYIkEgmMGjUK27Zt6/BjDxo0CBs3bkQkEunwY/dGSEjuBdi2jS1btqCsrAyM3DUIgiAIgiCILgLnHHV1dRgyZAgMo+unS0okEkilUh1+3FAoRAJyB0JCMkEQBEEQBEEQBEFIur66hiAIgiAIgiAIgiA6CBKSCYIgCIIgCIIgCEJCQjJBEARBEARBEARBSEhIJgiCIAiCIAiCIAgJCclEm/HNN99g/vz5mD17Nk455RT069cPjDEwxnDRRRd1dveIFtDWv+mCBQswffp0DBs2DOFwGMOGDcP06dOxYMGCotvIZDKYM2cOjjvuOPTv3x/RaBSjR4/GFVdcgVWrVjW7T0Tr+PDDD3HLLbfg5JNPdn7X0tJSjB07FhdffDHefffdZrVH90jPoba2FvPmzcO1116L448/HmPGjEFFRQVCoRAGDBiAE044Abfffjuqq6uLau+9997DBRdcgJEjRyISiWDQoEGYOnUqnnzyyWb168knn8TJJ5+MQYMGIRKJYOTIkbjgggvw/vvvt+Q0iXbguuuuc+YaxhgWLVrU5D40dhR/3R5++GHPdoX+Hn744Zxt3HTTTUW3of5uuummdjt3gmhzOEG0EQDy/s2aNauzu0e0gLb6TS3L4pdeemnB9i677DJuWVbBdnbs2MEnTpyYt41wOMzvv//+Vp41USzHHXdcwd9U/V144YU8mUwWbIvukZ7H66+/XtT90a9fP/7KK68UbOvGG2/khmHkbeO0007jjY2NBduIx+P81FNPzduGYRj8pptuastLQLSAjz76iAcCAc9vs3Dhwrzb09ghaM51mzt3blHPJgA+d+7cnG3ceOONRbeh/p544on2uwAE0caQkEy0GfpAOGLECH7yySeTkNzNaavf9Prrr3f2O+SQQ/iTTz7Jly1bxp988kl+yCGHON/dcMMNedvIZDL82GOPdbadMWMGX7BgAV+6dCn/85//zAcMGOAsdF9++eU2OHuiKUaPHs0B8CFDhvBrrrmG//3vf+fLli3j77//Pr/zzjv50KFDnd9r5syZBduie6Tn8frrr/Phw4fzCy+8kP/pT3/izz33HH///ff5kiVL+FNPPcXPOeccbpomB8BDoRBfsWJFznbmzJnj/KajR4/mDz74IF+2bBl//vnn+aRJk4q+x7773e86206aNIk///zzfNmyZfzBBx907mUA/N57722Py0EUgWVZjqCqntemhGQaO5p/3XQh+dVXX+WffPJJ3r9du3blbGP79u0F9/vkk0/4ihUreHl5OQfAy8vLeTweb7+LQBBtDAnJRJsxe/Zs/uKLL/Jt27ZxzjnfuHEjCcndnLb4TdeuXetotw877LCsSbKhoYEfdthhHAAPBAL8s88+y9nOgw8+6Bz7xz/+cdb3n332mTMZjxkzhqfT6eadLNFsTjvtNP7UU0/xTCaT8/sdO3bwsWPHOr/b22+/nXM7ukd6JvnuC51//OMfzm82ffr0rO+rq6t5RUWFo6jbsWNH1jFOP/30JoWCN99809nm9NNPz+rbjh07+IgRIzgAXllZyWtqaoo/UaLN+MMf/sAB8H333ZffcMMNTf6uNHYImnvddCF548aN7davl19+2TnOpZde2m7HIYj2gIRkot0gIbnn0ZLf9Ec/+pGzz/vvv59zm/fff7/gAoVzzvfbbz8OgPfp04c3NDTk3Oa2225z2nn66aeL6h/Rvrz44ovOb3L11Vfn3Ibukd7NuHHjOCDcrv389re/dX6vJ598Muf+X331lWORPvXUU3Nuc8oppziC0ldffZVzmyeffNI51u23397yEyJaxBdffMFLS0s5AL5o0SKPO28+YY/GjpZdt44SknXvjXxKUoLoqlDiLoIg2g3OOV544QUAwL777osjjzwy53ZHHnkkxo0bBwB44YUXwDn3fL9u3TqsXr0aAHDuueciFovlbEdPJvaPf/yjtd0n2oBJkyY5rz///POs7+keIcrKygAAiUQi67vnn38eAFBeXo4ZM2bk3H/YsGE48cQTAQBvvvkm6urqPN/X1dXhzTffBACceOKJGDZsWM52ZsyYgfLycgB0b3QGV155Jerr6zFr1iwcf/zxTW5PY4egudeto6itrXV+n1GjRuG4447r5B4RRPMgIZkgiHZj48aN2LJlCwA0OXmr7zdv3oxNmzZ5vtMzJBdqZ9CgQRg7diwAYMmSJS3pMtHGJJNJ57Vpmlnf0z3Su1m7di1WrFgBQAg6OqlUCsuWLQMAHHXUUQiFQnnbUb95MpnEhx9+6Plu+fLlSKVSnu1yEQqFHEFr+fLlSKfTzTsZosU8/fTTmD9/Pvr06YPf//73Re1DY0fLrltH8cwzz6CxsREA8P3vfx+MsU7uEUE0DxKSCYJoNz799FPntX8B7Ef/Xmn1W9POV199hYaGhqL7SrQPb7/9tvN6v/32y/qe7pHeRzwex2effYY777wTxx9/PDKZDADgJz/5iWe7devWwbIsAB1/b2QyGXz22WeFT4RoE3bv3o1rrrkGAPDb3/4W/fr1K2q/3j52tPS6+bn44osxZMgQhEIh9OvXD0ceeSR++ctfYvPmza3q36OPPuq8vvDCC1vVFkF0BiQkEwTRbnz99dfO63wujorhw4c7r7/66qtWt8M59+xHdDy2beM3v/mN8/7cc8/N2obukd6BXpe1pKQEY8eOxbXXXovt27cDAK6//np873vf8+zTmfdGrnaI9uEXv/gFtm3bhmOOOQaXXnpp0fv19rGjpdfNz6JFi7B161ak02lUV1dj6dKluPXWWzFmzBjce++9LWpz06ZNWLx4MQDgmGOOwejRo1vcP4LoLAKd3QGCIHouemxgaWlpwW1LSkqc1/X19e3SDtGx/OEPf3DcZWfMmIFDDz00axu6R3o3Bx98MO677z5MnDgx6zu6N3o+ixcvxgMPPIBAIIA5c+Y0yyW3N98frbluir333hszZszAUUcd5Qj/GzZswLPPPou///3vSCQS+OEPfwjGGC6//PJmtf3YY485sd+zZs1qdt8IoitAQjJBEO2GnoinUDwhAITDYee1imNq63aIjuPtt9/G9ddfDwAYMGAA7rnnnpzb0T3SOzjrrLNw2GGHARDX/PPPP8fTTz+Nf/zjH5g5cyb++Mc/Ytq0aZ596N7o2aRSKVx++eXgnOOnP/0pDjjggGbt31vvj9ZeNwCYPn06Zs2alSVcT5w4Eeeddx7mz5+PGTNmIJ1O46c//SnOOOMMDBo0qOj2H3/8cQBAJBLJ6UFEEN0BcrcmCKLdiEQizmuVOCcfeoKnaDTaLu0QHcOqVaswffp0ZDIZRCIRPPPMMxgwYEDObeke6R1UVlbigAMOwAEHHICJEyfiu9/9Lp577jk8+uij2LBhA84880w8/PDDnn3o3ujZ/PrXv8aaNWswYsQI3Hjjjc3ev7feH629bgBQUVFR0Po8bdo0zJ49G4DIIfDggw8W3fYHH3yAdevWAQDOPPNMVFRUtKiPBNHZkJBMEES7oUq7AE27pulJUPwub23VDtH+bNy4ESeffDJ27doF0zQxb948fPvb3867Pd0jvZvvf//7OOecc2DbNq666irU1NQ439G90XNZs2YNbrvtNgDAX/7yF48bc7H0xvujLa5bsVx++eWOIK0nYGwKSthF9BTI3ZogiHZDT4LSVJITPZmKnmQlVzuFsniqdhhjTSZhIdqWLVu24MQTT8SWLVvAGMNDDz2EM888s+A+dI8QZ555Jp5++mk0NDTglVdecRJ4tde9ody+m9sO0Xb84Q9/QCqVwt577414PI558+ZlbfPvf//bef3WW29h27ZtAIDTTz8dJSUlvXLsaIvrViwDBgxA3759sXPnzqIzXadSKTz11FMAgIEDB2Lq1KlFH48guhokJBME0W7sv//+zus1a9YU3Fb/3l8qyN/OwQcf3GQ7w4cPb1ctO+Fl586dOOmkk7BhwwYAwspRjBWB7hGif//+zusvvvjCeT127FiYpgnLstr03iimnUAggH322afpzhMtQrktb9iwATNnzmxy+1/96lfO640bN6KkpKRXjh1tcd2aQ3MTgs2fP9/xBjn//PNhmmaz9ieIrgS5WxME0W6MGjUKQ4YMAdC0u9Y777wDABg6dCj22msvz3fHHnus87pQO9u2bXNioY455piWdJloAXv27MHUqVOdeqO/+c1vcOWVVxa1L90jhG6l0l1YQ6EQDj/8cADA+++/XzBeVP3m4XA4y1I8ceJEJyFToXsjlUrhgw8+cPYJBoPNPBOiI6Gxo33ZsWMHdu7cCQDOdW4KcrUmehIkJBME0W4wxhx32zVr1jgLUD8ffPCBo6E/88wzs7TXY8eOdbT/Tz/9NOLxeM529MQ/06dPb233iSKIx+M47bTT8K9//QsA8D//8z+47rrrit6f7hHimWeecV4feOCBnu/OOussAEBtbS2ee+65nPt//fXXeOONNwAAU6ZM8cSYAiLmdMqUKQCAN954I69r7nPPPYfa2loAdG+0Nw8//DA45wX/9KRUCxcudD5XQm5vHDva4roVy3333eeUcTr++OOb3L66uhovv/wyAGDChAmYMGFCs45HEF0OThDtxMaNGzkADoDPmjWrs7tDtAEt+U3Xrl3LTdPkAPhhhx3G4/G45/t4PM4PO+wwDoAHAgG+bt26nO08+OCDzrGvvPLKrO/Xr1/Py8vLOQA+ZswYnk6nm31+RPNIJpP85JNPdn6Xa665pkXt0D3SM5k7dy5vbGwsuM2dd97p/GajRo3imUzG8311dTWvqKjgAPjIkSP5zp07Pd9nMhl++umnO20sXLgw53HefPNNZ5szzjgj6zg7duzgI0aM4AB4ZWUlr6mpaf4JE23KjTfe2OTvSmNHNk1dt40bN/J//etfBdt48cUXeSgU4gB4NBrlX3/9dZPH/ctf/uIc94477mhp9wmiy0BCMtFmLF68mM+dO9f5+93vfucMmMccc4znu7lz53Z2d4kiaKvf9Prrr3f2O+SQQ/i8efP48uXL+bx58/ghhxzifHfDDTfkbSOTyfBjjjnG2fbss8/mr7zyCl+6dCn/y1/+wgcMGMABcMMw+Msvv9wOV4PwM2PGDOf3mDx5Mv/444/5J598kvdv7dq1eduie6TnMXLkSN6nTx/+gx/8gD/yyCP83Xff5StWrOCLFy/md999t+e3CoVC/PXXX8/Zzpw5c5ztRo8ezR966CG+fPly/sILL/BJkyY5382cObNgf7773e86206aNIm/8MILfPny5fyhhx7io0ePdr6799572+NyEM2kGCGZcxo7/DR13RYuXMgB8KOOOor/+te/5i+99BJfvnw5X758OX/qqaf4OeecwxljTht//etfizruxIkTHWXEtm3b2visCKLjISGZaDNmzZrlDKrF/BFdn7b6TS3L4pdccknBfS+99FJuWVbB/uzYscOZiHP9hcNhfv/997f1ZSDy0Jx7Q1kC80H3SM9j5MiRRd0Xw4YN46+99lrBtmbPnu1ZuPv/Tj311Cat1vF4nJ966ql52zAMg994441teAWI1lCskExjh5diheSm/mKxWNEKo9WrV3ueRYLoCVB2a4Ig2h3DMPDggw/i7LPPxn333Yfly5dj586d6NevHyZOnIgrrrgCp5xySpPt9OvXD++99x7uv/9+PPHEE1i9ejUaGhowZMgQTJkyBddccw3Gjx/fAWdEtDV0j/Q8Xn31Vbz00ktYsmQJ1q9fj+3bt6O6uhrRaBQDBgzAwQcfjGnTpuHcc89FLBYr2NbNN9+MqVOn4q9//SsWL16M7du3o7KyEhMmTMDFF19cVKbfaDSKl156CU888QQefvhhrFy5Ert378bAgQNx3HHH4aqrrsJRRx3VVqdPdBA0djSPQw89FI8//jjef/99fPjhh9i6dSt27tyJTCaDqqoqjB8/HlOmTMFll12GAQMGFNXmY4895rymhF1ET4FxLqPyCYIgCIIgCIIgCKKXQ9mtCYIgCIIgCIIgCEJCQjJBEARBEARBEARBSEhIJgiCIAiCIAiCIAgJCckEQRAEQRAEQRAEISEhmSAIgiAIgiAIgiAkJCQTBEEQBEEQBEEQhISEZIIgCIIgCIIgCIKQkJBMEARBEARBEARBEBISkgmCIAiCIAiCIAhCQkIyQRAEQRAEQRAEQUhISCYIgiAIgiAIgiAICQnJBEEQBEEQBEEQBCEhIZkgCIIgCIIgCIIgJCQkEwRBEARBEARBEISEhGSCIAiCIAiCIAiCkJCQTBAEQRAEQRAEQRASEpIJgiAIgiAIgiAIQkJCMkEQBEEQBEEQBEFISEgmCIIgCIIgCIIgCAkJyQRBEARBEARBEAQhISGZIAiCIAiCIAiCICQkJBMEQRAEQRAEQRCEhIRkgiAIgiAIgiAIgpCQkEwQBEEQBEEQBEEQEhKSCYIgCIIgCIIgCEJCQjJBEARBEARBEARBSEhIJgiiS7Np0yYwxsAYw1577dXZ3cnLokWLnH6ecMIJnd2dglRXV+Pmm2/GEUccgaqqKpim6fT94Ycf7uzuEQRuuukm55686aabOrs7HcoJJ5zgnPuiRYs6uzsEQRC9EhKSCYIA4F2Y9bZFaW9iw4YNmDBhAm666SYsW7YMu3fvhm3bzW5Hv1/a4q8rK0A6ir322ivruqxcubJZbZxzzjlZbZDigyAIgiCaR6CzO0AQBEF0HFdccQU2b94MAIhGozjxxBMxdOhQmKYJANhvv/06s3uEj0cffRR33HFHUdvu2rULL774Yjv3iCAIgiB6PiQkEwRB9BK2bt2KN954AwAQDoexcuVK7LPPPi1qa/r06TjggAPyfl9XV4dHH33UeX/hhReirKws7/Z9+/ZtUT96Ok888QRuv/12R4lRiKeeegrJZLIDekUQBEEQPRsSkgmCIHoJH330kfP6uOOOa7GADADXXHNNwe83bdrkEZJvvvlmcqluBvvvvz8+/fRTbNu2Da+99hpOOeWUJvdR1zsUCmHEiBFYv359e3eTaAcoDpkgCKLzoZhkgiCIXsKuXbuc14MHD+7EnhBN8f3vf995rSsb8vHZZ5/h/fffBwCceuqpZJknCIIgiFZAQjJBEEQvIZ1OO68Ng4b/rszBBx+Mgw46CADwwgsvoLa2tuD2uiA9a9asdu0bQRAEQfR0aJVEEESbk06n8eqrr+IXv/gFJk2ahCFDhiASiSAajWLYsGE45ZRT8Mc//hH19fUtPsabb76JmTNnYvTo0YhGo+jfvz+OO+443HXXXc2Oy2xoaMA999yD008/HSNHjkQsFkNZWRn22WcfXHLJJXjrrbda3M+2oL6+Hn/+858xdepUDBs2DJFIBFVVVTjggANw1VVXYenSpXn31UtTXXzxxc7njzzySFYW5IsuuqgDzsZLvhJf7777Li677DLsu+++qKioAGMMP/nJT5zvm1siqCUlulavXo3//u//xuGHH46BAwciFAqhf//+OOKIIzB79mxs2bKleSfbTC688EIAQGNjI5555pm823HO8fjjjwMQsd2nnXZai463fPly/PSnP8XBBx+M/v37IxQKYdCgQTj++OPx29/+1uOJUAyJRAIPPfQQzj33XIwePRrl5eUIhUIYMGAAjjvuOFx//fUF710/mUwGjz76qJNsLhwOY/DgwTjrrLMwf/78otqwbRuLFy/G7NmzcfLJJ2PEiBGIxWJOW5MnT8att96KnTt3FtWe/vwo1q5di5/85CfYb7/9UFpaivLyckyYMAE33HBDUe0WWwIqnU7j8ccfx4wZM7D33nujtLQUgUAAZWVlGDNmDKZOnYrZs2dj2bJlOffP90zMnz8fM2bMwF577YVIJIK+ffvilFNOwcsvv5zVhm3beOGFFzBt2jSMGjUKkUgEgwcPxjnnnIMPPvigyXMlCILosnCCIAjO+fHHH88BcAD8xhtvbHE7X375Je/bt6/TVqG/vn378tdee61gexs3bnS2HzlyJE+lUvzyyy8v2O5+++3H165dW1R/n376aT5o0KAm+zpt2jS+e/fuvO0sXLjQ2fb4449vziUryIsvvlhU/773ve/xhoaGgv1q6m/WrFlt1m/9dwPAN27c2OR2I0eO5Mlkkl9xxRU5+3fNNdc4+914443Nul+b8/skEgl+xRVXcNM0C16vaDTK//KXvxR/UZpg5MiRTtsLFizgW7dudfrw7W9/O+9+ixYtcva78sorOeecH3HEEc5nc+fOLXjcmpoafvbZZzd5f1RWVvJnnnmmqHN59tln+dChQ4u67+65556s/f2/79dff82PPvrogu1cfPHF3LKsvH1KpVJF96mkpIQ/9thjTZ6nvg/nnN9zzz08HA4XHPOWL19esE19LF64cGHObdauXcv322+/op/tzz77LKsN/zPR0NDAv/vd7xZsR3/Wvvnmm4K/CWOsTZ8PgiCIjoQSdxEE0aY0NDSguroaAFBVVYXx48dj5MiRKC0tRSqVwsaNG/HBBx8gkUiguroap556Kt5++20cffTRRbV/3XXX4b777gMAHHTQQTj44IPBOcc///lPfPrppwCEBXDy5Ml4//33MXz48Lxt/eEPf8C1114LzjkAoLy8HEcddRSGDRsGy7KwatUqfPjhh+CcY/78+TjhhBOwZMkSxGKx1lyionnqqadw/vnnw7IsAIBpmjj22GMxZswY1NfXY/HixY4184knnsDGjRvx1ltvIRKJOG0MHToUV155JQBgzZo1ePPNNwEA++67L6ZMmeI53pFHHtkRp1WQn/70p7j33nsBAAceeCAmTJiAYDCIdevWdYiLeENDA6ZOnYolS5Y4n40ePRqHHnooqqqqUFNTgyVLlmDLli1obGzE1VdfjdraWvz3f/93m/dl0KBBOOmkk/DKK69g8eLF2LRpU87kZ4888ojzWlmfi2Xbtm2YPHkyVq9e7Xw2fvx4TJgwAaWlpfjmm2+wePFiVFdXY/fu3Tj33HPx2GOP4fzzz8/b5h133IGf//znznPFGMNBBx2E8ePHo7S0FDU1Nfjkk0+wdu1aAMLiXIj6+np85zvfwb///W/EYjEcd9xxGD58OOrq6rBw4UJ88803AIC5c+di3LhxuO6663K2Y1mWU/6stLQU48ePx957743y8nKk02l8/fXX+OCDD1BbW4uGhgZ8//vfRzAYxHnnnVfUtXz44Yfxox/9CAAwbtw4HHbYYYhGo1izZg2WLFkCzjmqq6txxhlnYPXq1aioqCiqXT91dXU48cQT8dVXXwEQoROHHHKIY7mOx+PYvHkzVq5cWbRFHAAuvfRSzJs3D4FAAMcccwzGjBmDeDyOt956C9u3bwcgEvCNGzcOZ511Fk4++WSsWLECkUgE3/72tzFixAjs3r0bb775Jnbt2gXOOf7zP/8Thx56KI466qgWnStBEESn0ZkSOkEQXYe2siRv2rSJX3311Xzp0qV5rTp79uzh1157rXO8sWPH5t1WtzQGg0HHGvPqq69mbft///d/vLy83Nl+6tSpefv5xhtvcMMwOAAeCoX4b37zm5yW2I8++ojvv//+Tps/+tGPcrbX1pbk9evX89LSUqfNww8/PMsaZFkWv+OOO5zzAMCvvvrqvG3OnTu3XazGuWiJJVlZTYcPH87feeedrG0TiYTzur0syRdeeKHnvsxlyctkMvzuu+92LIamafL33nuvyT40hd+SzDnnTzzxhPPZLbfckrVPPB7nZWVlHADfd999nc+LsSRblsUnTZrkucf+9a9/ZW3X2NjIb7rpJs4Yc6ysGzZsyNnmSy+95GwHgE+ePJl/+umnObfdsGED/9///V/+8MMPZ32n/77qOs+aNYtXV1d7tmtoaOAzZ850ti0tLeX19fU5j5dMJvnFF1/MFy5cyFOpVM5tEokEv/3223kgEHCs53V1dTm35dxrSQ6Hw7x///7Ob6fz9ttve8amm2++OW+bTVmS//jHPzrf77///nzNmjU527Ftmy9btoz/6Ec/4l9++WXW9/ozoa7x0UcfzT///HPPdvF4nJ9zzjnOtvvssw+/+uqrOQA+ffp0vn37ds/2NTU1/Nvf/raz/aRJk/KeK0EQRFeFhGSCIDjnbSckN4cf/vCHzjFffvnlnNv4hS3DMPiSJUvytvn66697tn/zzTeztrEsi++zzz7ONs8991zBfm7dupUPHDjQEdS/+uqrrG3aWkjWhbUxY8YUdPW+8847PdcnnwDT1YVkADwWixXlKt8eQvI777zjbDN69Gi+Y8eOgm3q1/M73/lOk31oilxCcjwed4SrffbZJ2ufv/3tb84+v/71r53PixGSH330UWebI488ksfj8YL906/5D3/4w6zv0+k032uvvZxtpk2bxtPpdDOuQO5jAeAzZ87Mu21jYyMfPny4s+28efNadEyd3/zmN057d999d97t/ELyypUr82571113OdvqCg0/TQnJumv866+/3qzz0vGHYowbNy6vgqG2tpb36dPHs/3kyZPzKjc3bdrkKL0YY3zr1q0t7idBEERnQIm7CILoNPREUm+88UZR+5x//vkFXbNPPPFEzJgxw3l///33Z23z4osv4rPPPgMAnHXWWZg+fXrBYw4aNMhJGpVOp/H0008X1deWsnv3bjz11FPO+9tvv72ga+Y111yD8ePHAxCJdJQ7enfkqquuwtixYzvl2Hfeeafz+o477kC/fv0Kbn/RRRdh3333BQC8+uqrTphBWxKNRnHOOecA8JZ5Uqis1oZh4IILLmhW2/r5zpkzB9FotOD2119/PSorKwEATz75JGzb9nz/7LPPYtOmTQCAkpISzJ07F4FA66O6QqGQp69+IpEIZs6c6bzPl6iqObRkbLr88sudjOS5uPDCC53rsXbt2iYzludD369///4taiMXv/nNb1BSUpLzu7KysqyEcHfeeWfeEIiRI0c64zTnHB9++GGb9ZMgCKIjoJhkgiDajXQ6jaVLl2LlypXYtm0b6urqkMlknO/r6uqc1ytWrCiqzWJiLmfNmoXnnnsOALBw4cKs7/Usrd/73veKOu7kyZOd1++++y5+9rOfFbVfS3jvvfecDN39+vXD6aefXnB7wzBwySWX4NprrwWQ+5y7C9/97nc75biZTAavv/46ABGbPm3atKL2mzRpEtasWQPOOZYsWYIzzjijzft24YUX4sEHHwQghGIV37l161ZHgDvhhBMKxt/72bp1q/PM7b///pgwYUKT+0QiERx11FFYsGAB9uzZg3//+98eofCVV15xXs+cObNJJUOxHHvssRg0aFDBbQ455BDntRLUC2HbNv75z39ixYoV+Prrr1FbW+spkaZT7NiklBn5KCsrw+jRo7F27VpwzvHFF1/gwAMPLKptHf13njNnDu65555mt+EnGo02mRVd7+uYMWOavGcOOOAALF68GACwcePGVveRIAiiIyEhmSCINqexsRG//vWvMWfOnKITxxSzHWMMRxxxRJPb6Ulitm/fjq1bt2Lw4MHOZ7o17tlnn8Xbb7/dZJt79uxxXquEOe3FRx995Lw+/PDDi7LGHXPMMZ79OeeesjTdgWAw2CKhoS34+OOP0dDQ4PTjmmuuKWq/5cuXO6/b67447rjjMGrUKGzcuBFPPfUU/vSnPyEUCuHxxx93kro1N2GX/gw0NjbiqquuKmq/zz//3Hn91VdfeYRkveTPpEmTmtWfQhRzT/Tt29d5XchCm8lk8Oc//xl/+MMf8PXXXxd1/GLHsLbsZyHOPfdcPPTQQwCEkPzPf/4Ts2bNwtSpUzFmzJgWtTl27FgEg8GC21RVVTmvledKIfr06eO8bum5EgRBdBYkJBME0abs2rULkydPLtr6otCtyvmoqqpCWVlZk9v1798fkUjEyZq7Y8cOj5Cs17fV3ZqLpbn1YpvLjh07nNcjR44sah8963EqlUJdXR3Ky8vbumvtSlVVVZu457YE/Z6orq7GX//612a30V73BWMMF1xwAX71q19h165dePHFF3H22WfjscceAyBcm88+++xmtamf78aNG9vkfFUGZADYe++9m91ePorJAq0LePkswslkEmeccQZee+21Zh2/mLEJaLt+NsXUqVNx9dVX4y9/+QsAoahRypqBAwfi2GOPxQknnICzzjoLw4YNK6rNYvquP5vN3b6l50oQBNFZUEwyQRBtypVXXukIyKFQCJdddhleeOEFrFu3znG35iJpoMcFzx/fmIvmlF7SY+v8i1zdKtwSdJfx9qC+vt55nS9G0I9/u2IX9l2JpmJi25PW3hNA+94XuqX40UcfxUcffYRPPvkEADBjxgyUlpY2q732OF/9nmtufwrRVh4RN998syMgM8Zw3nnn4emnn8bq1auxZ88epFIpZ2zisnwVAM/rjuhnMfz5z3/Gc889h8MPP9zz+fbt2/Hss8/i6quvxogRI/Af//Ef+PLLL5tsr7l9725eKgRBEM2FLMkEQbQZmzdvxrx58wCIONlXXnmloNtlcwW5eDxe9LbKdRZAlvW5pKTEERL+9a9/eeIZuwK6gKGfRyH82xVjce9NNKWE0ZUMBx10EFauXNneXWoWY8aMwdFHH4333nsPCxYs8Py+zXW1Brzne8YZZ+CFF15odR/Lysoc67Ku6OkKJJNJx/IKiJrGha5bd1AyTZ8+HdOnT8eXX36JRYsW4b333sPixYudevGcczz77LPOd52VEI8gCKI7QpZkgiDajLfeesuxupxyyilNxiV+8cUXzWp/165dRS2+d+7c6bhaA8hKIDRw4EDn9bZt25rVh45Az1hbjBUI8CYrCoVCPV5I1t1Wi7HgNmU57er3BOAKw+l0Gn/7298AAMOGDfMklSuW9jhfvc2ulqhp2bJlztgxfvz4JhULzR2bOpMRI0bgwgsvxJw5c7Bq1Sp8+eWXuPnmmx3Pm+rq6nZNNEgQBNETISGZIIg2Q49zLCaJzTvvvNOs9jnnWLp0aZPb6UmJBg4ciCFDhni+15N/LVmypFl96Ah0y/ayZcuc5EyFeO+99zz793R3SD3eupjSS8o1OR8HH3wwwuEwAOCbb77B+vXrW9fBduC8885z+qg4//zz85bhKYT+DKxYsaJoj4VCHHnkkc7rt956q9XttSXtPTZ1JYYPH47Zs2d7SsG99tprTsZ8giAIomlISCYIos3QF+tNuUbH43GnxmtzUMmKCqG3m8uarZf3eeihhzxW567A0Ucf7QhDO3bswEsvvVRwe9u2MXfuXOd9SyyL3Q09UVkxSeKaqm0djUY91+3uu+9uadfajcrKyqxyYC1xtQZEYq399tsPgEj0pkpMtYZTTjnFeT1v3ryis0J3BM0Zm7p7rXGFXo4snU6jpqamE3tDEATRvSAhmSCINkPPaPvyyy8XtIBee+21nmy4xfL4448XtCYvXLgQzz77rPP+sssuy9rm7LPPdkqlbN26FT/+8Y+LTs5TX1/fJla3QlRWVuK8885z3v/85z8vGCN51113OZZSwzBw+eWXt2v/ugITJ050rOVLly7F6tWr82579913Y9WqVU22ed111zmv//KXvzg1iIuho1y077rrLieb8b/+9S/sv//+LW5LP99f/vKXTVrbdXKd74wZM5xs7PX19bj44ovbPcldsehj09tvv13Q/f53v/tdl4tJ1ylW+aCXJDMMw1N+iiAIgigMCckEQbQZkydPduLg1q9fj1mzZmH37t2ebWpra3H55Zdjzpw5RWduVgSDQViWhWnTpuUUYF566SVMnz7dEXhPOukkTJkyJWs70zRxzz33wDRNAMDcuXNx2mmnFRS0VqxYgeuuuw7Dhw/vkHjL2bNnOwm81q1bh6lTp2LDhg2ebWzbxp/+9CdPvOGVV17psbL2VAYNGuRYfjnnmDlzZlbd20wmgzvuuAP/+Z//meWmnIvjjz8es2bNcvY97bTTcNttt+WNg08kEnj++edx5plneqx27cnAgQNx2GGH4bDDDmt1wrkLLrjAuYZ1dXU49thjce+99yKVSuXcvra2Fn/7299wwgkn4Oqrr876PhAI4K677nKUF/Pnz8fUqVOxZs2anO1t2rQJs2fPbpFHSXM55JBDMHToUAAiPv2cc87xuGADIrnX7Nmzcf311zd7bOpIjjrqKHzve9/DggUL8v5W69atc+5lAJgyZQpCoVBHdZEgCKLbQ9mtCYLIYs6cOXj++eeL3v6WW27BGWecgaqqKvzXf/0XbrnlFgDA3/72NyxYsABHHHEEhg4diq1bt2LRokVoaGhAIBDA3Xff7VnINcWQIUMwffp0/PGPf8RJJ52ECRMm4OCDDwbnHP/85z891sLBgwfj/vvvz9vWiSeeiHvuuQc/+tGPYFkWFixYgFdeeQX7778/DjroIJSXlyMej2Pr1q1YuXKlp3ZxRzB69Gg88MADOP/882FZFt5//32MGzcOxx13HEaPHo36+nosXrwYmzdvdvY58sgjcfvtt3doPzuTW2+9FQsXLoRt21i5ciXGjh2LyZMnY+jQoaipqcE777yDb775BqWlpbjttttyCnZ+7r33XmzduhWvvfYaUqkU/vu//xv/7//9PxxxxBEYMWIEwuEwdu/ejc8//xz//ve/nTjPQw89tL1Pt80xTRNPP/00TjrpJHz00Ueora3FD3/4Q/ziF7/AUUcdhaFDh8I0TezatQtr167F6tWrHctwvrrM06ZNw2233Ybrr78egIhN3n///TFhwgSMHz8epaWlqKmpwccff4y1a9cCAP7whz+0+7kahoFf/epXuOSSSwAAr7/+OsaOHYujjz4aI0eORHV1NRYtWuRk577vvvtw/vnnt3u/WkI6ncaTTz6JJ598EtFoFAcddBD23ntvlJeXY9euXdiwYQM+/PBDZ/toNIrf//73ndhjgiCI7gcJyQRBZLF9+/ZmuULrsW6zZ8/Gpk2bHOtQTU0NFixY4Nm+srISc+fOxcEHH9zsvt1+++2oq6vDgw8+iJUrV+Z0ixw3bhyef/55x/UzHz/4wQ8wZswYXHHFFfjss8/AOceqVasKuuaOHz8effr0aXa/W8J5552HkpISXHbZZdi+fTsymQwWLlyIhQsXZm07c+ZMPPDAA4hEIh3St67AEUccgfvvvx+XX345LMtCY2NjVvz24MGD8dRTTxWV/AwAwuEwXn75Zdx888244447EI/HEY/Hc15zRTAY9CSt6k707dsXS5Yswc9+9jM88MADyGQyqK2txauvvpp3n2g0WlApcN1112GvvfbCNddcg+3bt4NzjhUrVuSNHe8oq+3FF1+M9evX49e//jUAUTbt9ddf92wTiUTwxz/+Ed/73ve6rJCsZ65vbGzE0qVL84agjBo1Co8//jgOOuigjuoeQRBEj4CEZIIg2hTTNPHII4/gnHPOwX333YelS5di165dqKqqwogRI3DmmWfikksuwZAhQzxli4olGAzigQcewDnnnIMHH3wQy5cvx9atW1FSUoL99tsP5513Hi6//PKi3GsBkdhr9erVeP755/HSSy/hgw8+wLZt21BbW4tYLIaBAwdi3333xdFHH41TTjmlRYJ9a5g2bRrWr1+Phx56CPPnz8eqVauwc+dORKNRDBkyBJMmTcKFF17oyVbcm7jkkktw5JFH4s4778Rbb72FrVu3IhKJYNSoUTj77LNxxRVXoF+/fli0aFHRbZqmiVtuuQVXX301Hn30Ubzxxhv49NNPsXPnTqTTaZSXl2PkyJE48MADMWnSJJx66qmesl3djWg0invuuQfXXXcdHn/8cbz11ltYt24dqqurYds2KioqsPfee2PChAmYMmUKvvOd73iyi+fivPPOw7Rp0/Doo49iwYIFjjeGZVmoqqrCuHHjcOyxx+I//uM/OrRO+a233opTTjkFd911F959913s2LEDZWVlGDZsGL7zne/g0ksvxT777NNh/WkJK1aswAcffICFCxdi2bJlWLt2LbZs2YJ4PI5YLIZBgwbh4IMPxhlnnIFzzz236LGQIAiCcGG82Gw1BEEQBEEQBEEQBNHDocRdBEEQBEEQBEEQBCEhIZkgCIIgCIIgCIIgJCQkEwRBEARBEARBEISEhGSCIAiCIAiCIAiCkJCQTBAEQRAEQRAEQRASEpIJgiAIgiAIgiAIQkJCMkEQBEEQBEEQBEFISEgmCIIgCIIgCIIgCAkJyQRBEARBEARBEAQhISGZIAiCIAiCIAiCICQkJBMEQRAEQRAEQRCEhIRkgiAIgiAIgiAIgpCQkEwQBEEQBEEQBEEQEhKSCYIgCIIgCIIgCEJCQjJBEARBEARBEARBSP4/iIFeA7EHOYUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1100x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the percentage of correct predictions for each unique pair of true and predicted values\n",
    "count_dict = {} \n",
    "for true_val, pred_val in zip(y_test_1D, KNN_predict_1D_40):\n",
    "    if (true_val, pred_val) not in count_dict:\n",
    "        count_dict[(true_val, pred_val)] = 1\n",
    "    else:\n",
    "        count_dict[(true_val, pred_val)] += 1\n",
    "# print(count_dict)\n",
    "\n",
    "# Calculate total counts for each value starting from 1 to 456 for key[0]\n",
    "total_counts = {}\n",
    "for i in range(1, 457):\n",
    "    total_counts[i] = sum(value for key, value in count_dict.items() if key[0] == i)\n",
    "# print(total_counts)\n",
    "\n",
    "\n",
    "# Calculate percentages\n",
    "percentages = {}\n",
    "for key, value in count_dict.items():\n",
    "    # print(key)\n",
    "    # print(value)\n",
    "    percentages[key] = (value / total_counts[key[0]]) * 100\n",
    "# print(percentages)\n",
    "\n",
    "# Extract x and y data for the scatter plot\n",
    "x_data = [key[0] for key in percentages]\n",
    "y_data = [key[1] for key in percentages]\n",
    "colors = [value for value in percentages.values()]\n",
    "\n",
    "plt.figure(figsize=(11, 8))  # Adjust width and height as needed\n",
    "\n",
    "# Create the scatter plot\n",
    "# plt.scatter(x_data, y_data, c=colors, cmap='viridis', s = 1)\n",
    "plt.scatter(x_data, y_data, c=colors, cmap='Reds', s = 3) #, zorder=1)\n",
    "\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Label of True Mechanism', fontsize = 24, labelpad=15)\n",
    "plt.ylabel('Label of Predicted Mechanism', fontsize = 24, labelpad=15)\n",
    "# plt.title('Scatter Plot')\n",
    "\n",
    "# Add color bar\n",
    "cbar = plt.colorbar(ticks=[0, 20, 40, 60, 80, 100])\n",
    "cbar.set_label('Percentage', fontsize=18)  # Adjust the fontsize and labelpad as needed\n",
    "# Set font size of ticks in color bar\n",
    "cbar.ax.tick_params(labelsize=16)  # Adjust the font size as needed\n",
    "\n",
    "# Add axis ticks\n",
    "plt.xticks([1, 100, 200, 300, 400, 457], fontsize = 20)  # Adjust the range and step size for x-axis ticks\n",
    "plt.yticks([1, 100, 200, 300, 400, 457], fontsize = 20)  # Adjust the range and step size for x-axis ticks\n",
    "plt.tick_params(axis='both', direction='in')  # Set ticks to be inside the plot\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "classifier_KNN_80 = KNeighborsClassifier(n_neighbors = 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Accuracy Scores: [0.01467682 0.0150906  0.01464692 0.01467686 0.01479391]\n",
      "Mean CV Accuracy: 0.014777021850982276\n"
     ]
    }
   ],
   "source": [
    "# Perform cross-validation\n",
    "cv_scores_80 = cross_val_score(classifier_KNN_80, X_train, y_train, cv=5)\n",
    "\n",
    "# Print cross-validation scores\n",
    "print(\"Cross-Validation Accuracy Scores:\", cv_scores_80)\n",
    "print(\"Mean CV Accuracy:\", cv_scores_80.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=80)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=80)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=80)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_KNN_80.fit(X_train, y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_predict_1D_80 = classifier_KNN_80.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27523789822093503"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_KNN_80.score(X_test,y_test_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data\n",
    "plt.scatter(y_test_1D, KNN_predict_1D_80, marker='o', s = 1, label='Data Points')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_test_predicted')\n",
    "plt.title('KNN_80')\n",
    "\n",
    "# # Adding a legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KNN_model_conc_normalized_80.joblib']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(classifier_KNN_80, 'KNN_model_conc_normalized_80.joblib', compress=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.     0.     0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.     ... 0.     0.     0.    ]\n",
      " ...\n",
      " [0.     0.     0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.     ... 0.     0.     0.0125]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba_KNN_80 = classifier_KNN_80.predict_proba(X_test)\n",
    "print(y_predicted_proba_KNN_80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_predicted_grouping(predictions):\n",
    "    threshold = 0.95\n",
    "    index = np.argsort(predictions) + 1\n",
    "    prob = 0\n",
    "    grouping = []\n",
    "    probabilities = []\n",
    "    for j in index[::-1]:\n",
    "        prob += predictions[j - 1]\n",
    "        grouping.append(j)\n",
    "        probabilities.append(predictions[j - 1])\n",
    "        if prob >= threshold:\n",
    "            break\n",
    "    # print(grouping)\n",
    "    # print(probabilities)\n",
    "    return grouping, probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.     0.     0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.     ... 0.     0.     0.    ]\n",
      " ...\n",
      " [0.     0.     0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.     ... 0.     0.     0.0125]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba_KNN_80 = classifier_KNN_80.predict_proba(X_test)\n",
    "print(y_predicted_proba_KNN_80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage: 96.27741001241208\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "total_iterations = len(y_predicted_proba_KNN_80)\n",
    "for idx_proba, i in enumerate(y_predicted_proba_KNN_80):\n",
    "    # print(idx_proba)\n",
    "    # print(y_test_1D[idx_proba])\n",
    "    grouping, probabilities = generate_predicted_grouping(i)\n",
    "    if y_test_1D[idx_proba] in grouping:\n",
    "        cnt += 1\n",
    "\n",
    "percentage = (cnt / total_iterations) * 100\n",
    "print(\"Percentage:\", percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_KNN_120 = KNeighborsClassifier(n_neighbors = 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform cross-validation\n",
    "cv_scores_120 = cross_val_score(classifier_KNN_120, X_train, y_train, cv=5)\n",
    "\n",
    "# Print cross-validation scores\n",
    "print(\"Cross-Validation Accuracy Scores:\", cv_scores_120)\n",
    "print(\"Mean CV Accuracy:\", cv_scores_120.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=120)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=120)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=120)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_KNN_120.fit(X_train, y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_predict_1D_120 = classifier_KNN_120.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2671079851055027"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_KNN_120.score(X_test,y_test_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data\n",
    "plt.scatter(y_test_1D, KNN_predict_1D_120, marker='o', s = 1, label='Data Points')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_test_predicted')\n",
    "plt.title('KNN_120')\n",
    "\n",
    "# # Adding a legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KNN_model_conc_normalized_120.joblib']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(classifier_KNN_120, 'KNN_model_conc_normalized_120.joblib', compress=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " ...\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.03333333 0.         0.05833333]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba_KNN_120 = classifier_KNN_120.predict_proba(X_test)\n",
    "print(y_predicted_proba_KNN_120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_predicted_grouping(predictions):\n",
    "    threshold = 0.95\n",
    "    index = np.argsort(predictions) + 1\n",
    "    prob = 0\n",
    "    grouping = []\n",
    "    probabilities = []\n",
    "    for j in index[::-1]:\n",
    "        prob += predictions[j - 1]\n",
    "        grouping.append(j)\n",
    "        probabilities.append(predictions[j - 1])\n",
    "        if prob >= threshold:\n",
    "            break\n",
    "    # print(grouping)\n",
    "    # print(probabilities)\n",
    "    return grouping, probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage: 97.14418700868845\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "total_iterations = len(y_predicted_proba_KNN_120)\n",
    "for idx_proba, i in enumerate(y_predicted_proba_KNN_120):\n",
    "    # print(idx_proba)\n",
    "    # print(y_test_1D[idx_proba])\n",
    "    grouping, probabilities = generate_predicted_grouping(i)\n",
    "    if y_test_1D[idx_proba] in grouping:\n",
    "        cnt += 1\n",
    "\n",
    "percentage = (cnt / total_iterations) * 100\n",
    "print(\"Percentage:\", percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_KNN_456 = KNeighborsClassifier(n_neighbors = 456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=456)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=456)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=456)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_KNN_456.fit(X_train, y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_predict_1D_456 = classifier_KNN_456.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22838229209764171"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_KNN_456.score(X_test,y_test_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data\n",
    "plt.scatter(y_test_1D, KNN_predict_1D_456, marker='o', s = 1, label='Data Points')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_test_predicted')\n",
    "plt.title('KNN_456')\n",
    "\n",
    "# # Adding a legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KNN_model_conc_normalized_456.joblib']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(classifier_KNN_456, 'KNN_model_conc_normalized_456.joblib', compress=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " ...\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.00438596]\n",
      " [0.         0.         0.         ... 0.04824561 0.02412281 0.0745614 ]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba_KNN_456 = classifier_KNN_456.predict_proba(X_test)\n",
    "print(y_predicted_proba_KNN_456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_predicted_grouping(predictions):\n",
    "    threshold = 0.95\n",
    "    index = np.argsort(predictions) + 1\n",
    "    prob = 0\n",
    "    grouping = []\n",
    "    probabilities = []\n",
    "    for j in index[::-1]:\n",
    "        prob += predictions[j - 1]\n",
    "        grouping.append(j)\n",
    "        probabilities.append(predictions[j - 1])\n",
    "        if prob >= threshold:\n",
    "            break\n",
    "    # print(grouping)\n",
    "    # print(probabilities)\n",
    "    return grouping, probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " ...\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.00438596]\n",
      " [0.         0.         0.         ... 0.04824561 0.02412281 0.0745614 ]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba_KNN_456 = classifier_KNN_456.predict_proba(X_test)\n",
    "print(y_predicted_proba_KNN_456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage: 98.91704592470005\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "total_iterations = len(y_predicted_proba_KNN_456)\n",
    "for idx_proba, i in enumerate(y_predicted_proba_KNN_456):\n",
    "    # print(idx_proba)\n",
    "    # print(y_test_1D[idx_proba])\n",
    "    grouping, probabilities = generate_predicted_grouping(i)\n",
    "    if y_test_1D[idx_proba] in grouping:\n",
    "        cnt += 1\n",
    "\n",
    "percentage = (cnt / total_iterations) * 100\n",
    "print(\"Percentage:\", percentage)"
   ]
  }
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